Data Clustering

Normally, The Internet grows rapidly and most useful in each domain but network vulnerability and intrusions are still an important issue that causes attacks. Attacks can immediately cause system down. Therefore, it is necessary to detect network attacks before they damage the whole system for that we used firstly dataset. Generally, Intrusion detection system can be deployed to detect network threats and attacks. A good system to detect the illegal user is to monitoring the packets and using the different algorithms, methods and applications which are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize in two groups, normal or threat. We consider network intrusion detection using fuzzy genetic algorithm to classify attacks in the datasets. Fuzzy rule is a machine learning algorithm that can classify network attack data and protect the system from damage, while a genetic algorithm is an optimization algorithm that can help finding appropriate fuzzy rule and give the optimal solution. And also a new approach of intrusion detection system based on neural network. In this paper, we have a Multi-Layer Perceptron (MLP) is used for intrusion detection system, which is better solution for the intrusion detection using weka tool. This algorithm uses the number of layers so it is more secure from the hacker. We consider both well-known KDD99 dataset and our own network dataset. The KDD99 dataset is a benchmark dataset which is already stored. While our network dataset is an online network data captured in actual network environment. We evaluate our IDS in terms of detection speed, detection rate and false alarm rate.

Cluster analysis in data processing could be a main application of business. This investigation describes to gift DBSCALE algorithmic rule that extends enlargement seed choice into a DBSCAN algorithm rule. And also describes the density primarily based cluster conception and also describes its hierarchical extra space OPTICS has been planeed recently, and one among inthe main triumphant approaches to cluster. Aim of this analysis work is to manoeuvre on the advances cluster. During this work the planned procedure focuses on decrease the quantity of seeds points and additionally reduce the execution time cost of looking out neighbourhood information. And hierarchical cluster procedure are often helpful to those attention-grabbing subspaces so as to calculate a latitude for north and south cities and additional calculate line of longitude of various cities and also clustered them. Keywords— Data mining, Data Clustering Analysis, Density Based Clustering, Optics Algorithm, DBSCAN, IDBS...

Cluster analysis in data mining is a main application of business. This Investigation describes to present NCDBC algorithm that extends expansion seed selection into a DBSCAN algorithm. And the DBSCAN Algorithm describes the density based clustering concept and also describes its hierarchical additional room OPTICS has been planned newly, and one of the mainly triumphant approaches to clustering. Aim of this research work is to move on the hightech clustering; mainly density-based clustering by identifying new challenges for density based clustering and proposing inventive for these challenges. In this work the proposed procedure focuses on decrease the number of seeds points and also reduces the execution time cost of searching neighborhood data. And A hierarchical clustering procedure can be useful to these interesting subspaces in order to calculate a Latitude for north and south cities and also calculate Longitude of different cities.

In this paper a fuzzy point symmetry based genetic clustering technique (Fuzzy-VGAPS) is proposed which can determine the number of clusters present in a data set as well as a good fuzzy partitioning of the data. A new fuzzy cluster validity index, FSym-index, which is based on the newly developed point symmetry based distance is also proposed here. FSym-index provides a measure of goodness of clustering on different fuzzy partitions of a data set. Maximum value of FSym-index corresponds to the proper clustering present in a data set. The flexibility of Fuzzy-VGAPS is utilized in conjunction with the fuzzy FSym-index to determine the number of clusters present in a data set as well as a good fuzzy partition of the data. The results of the fuzzy VGAPS are compared with those obtained by fuzzy version of variable string length genetic clustering technique (Fuzzy-VGA) optimizing XB-index. The effectiveness of the Fuzzy-VGAPS is demonstrated on four artificial data sets and two real-life data sets.

This paper proposes a novel particle swarm optimisation (PSO) algorithm using the concept of age of particles. Effective fitness of a particle depends both on its functional value and age. Age of a newly generated particle is taken as zero, and in every iteration age of each individual is increased by one. In this paper, a trapezoidal aging function is considered. The model aims to emulate natural swarm system in a more natural way. The effectiveness of this concept is demonstrated by cluster analysis. Results show that the model provides enhanced performance and maintains more diversity in the swarm and thereby allows the particles to be robust to trace the changing environment.

Efficient data collection in wireless sensor networks (SNs) plays a key role in power conservation. It has spurred a number of research projects focusing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes in a SN. Thus, we concentrate on selective queries, i.e., queries that request data from a subset of a SN. The task of optimal data collection in such queries is an instance of the NP-hard minimal Steiner tree problem. We argue that selective queries are an important class of queries that can benefit from algorithms that are tailored for partial node participation of a SN. We present an algorithm, called Pocket Driven Trajectories (PDT), that optimizes the data collection paths by approximating the global minimal Steiner tree using solely local spatial knowledge. We identify a number of spatial factors that play an important role for efficient data collection, such as the distribution of participating nodes over the network, the location and dispersion of the data clusters, the location of the sink issuing a query, as well as the location and size of communication holes. In a series of experiments, we compare performance of well-known algorithms for aggregate query processing against the PDT algorithm in partial node participation scenarios. To measure the efficiency of all algorithms, we also compute a near-optimal solution, the globally approximated minimal Steiner tree. We outline future research directions for selective queries with varying node participation levels, in particular scenarios in which node participation is the result of changing physical phenomena as well as reconfigurations of the SN itself.

Due to the dramatic increase of data volumes in different applications, it is becoming infeasible to keep these data in one centralized machine. It is becoming more and more natural to deal with distributed databases and networks. That is why distributed data mining techniques have been introduced. One of the most important data mining problems is data clustering. While many clustering algorithms exist for centralized databases, there is a lack of efficient algorithms for distributed databases. In this paper, an efficient algorithm is proposed for clustering distributed databases. The proposed methodology employs an iterative optimization technique to achieve better clustering objective. The experimental results reported in this paper show the superiority of the proposed technique over a recently proposed algorithm based on a distributed version of the well known K-Means algorithm (Datta et al. 2009) [1].

An e$cient clustering algorithm is proposed in an unsupervised manner to cluster the given data set. This method is based on regulating a similarity measure and replacing movable vectors so that the appropriate clusters are determined by a performance for the classi"cation validity. The proposed clustering algorithm needs not to predetermine the number of clusters, to choose the appropriate cluster centers in the initial step, and to choose a suitable similarity measure according to the shapes of the data. The location of the cluster centers can be e$ciently determined and the data can be correctly classi"ed by the proposed method. Several examples are considered to illustrate the e!ectiveness of the proposed method.

Data clustering can find similarities and hidden patterns within data. Given a predefined number of groups, most partitional clustering algorithms use representative centers to determine their corresponding clusters. These algorithms, such as K-means and optimization-based algorithms, create and update centroids to give (hyper) spherical shape clusters. This research proposes a noncentroid-based discrete differential evolution (NCDDE) algorithm to solve clustering problems and provide non-spherical shape clusters. The algorithm directs the population of discrete vectors to search for data group labels. It uses a novel discrete mutation strategy analogous to the continuous mutation in classical differential evolution. It also combines a sorting mutation to enhance convergence speed. The algorithm adaptively selects crossover rates in high and low ranges. We use the UCI datasets to compare the NCDDE with other continuous centroidbased algorithms by intra-cluster distance and clustering accuracy. The results show that NCDDE outperforms the compared algorithms overall by intra-cluster distance and achieves the best accuracy for several datasets.

Application uses URL as contribution for Web Application Vulnerabilities recognition. if the length of URL is too long then it will consume more time to scan the URL (Ain Zubaidah et.al 2014).Existing system can notice the web pages but not overall web application. This application will test for URL of any length using String matching algorithm. To avoid XSS and CSRF and detect attacks that try to sidestep program upheld arrangements by white list and DOM sandboxing techniques (Elias Athanasopoulos et.al.2012). The web application incorporates a rundown of cryptographic hashes of legitimate (trusted) client side contents. In the event that there is a cryptographic hash for the content in the white list. On the off chance that the hash is discovered the content is viewed as trusted or not trusted. This application makes utilization of SHA-1 for making a message process. The web server stores reliable scripts inside div or span HTML components that are attribute as reliable. DOM sandb...

Several games nowadays try to improve the player immersion by representing human behavior as real as possible, generally using agent technologies to model non-player characters (NPCs). However, agent-based behavioral models representing the existing complexity of, for instance, a decision-making for a real life situation can become a very intensive computing task. For this reason, real-time simulation-based games may benefit from optimizations produced on how NPCs react to changes in the simulated game world. This paper presents an approach for speeding up the decision-making of autonomous agents representing NPCs of a game. The optimization is reached by bounding the agent perception to a subset of all agent surrounding elements, which contains only the most important elements for the agent at current time. In other words, the agent is modeled as having “focus of attention”. The attention focus represented in this work is based on theories of emotions and personality.

Modularity, hierarchy, and interaction locality are general approaches to reducing the complexity of any large system. A widely used principle in achieving these goals in designing software systems is striving for high cohesion within a module and low coupling between modules. However, this principle has difficulties in practice. Because a hierarchical system structure often consists of several layers, it is difficult to decide at what layer an interaction should be considered as cohesion, and at what layer an interaction should be considered as coupling. In this paper, we do not differentiate cohesion and coupling, but use a general term interaction to represent the dependencies between software modules. We propose a method to verify the design modularity, hierarchy, and interaction locality of a software system. This approach is based on the component interactions gathered from certain design level artifacts, such as UML diagrams. Data clustering technique is then used to group software components according to the degree of interactions between them. To show how to use this approach, we apply it to Parna's KWIC object-oriented design example, in which sequence diagram is used to derive the degree of component interactions.

In this paper, a previously introduced data mining technique, utilizing the Mean Field Bayesian Data Reduction Algorithm (BDRA), is extended for use in finding unknown data clusters in a fused multidimensional feature space. In the BDRA the modeling assumption is that the discrete symbol probabilities of each class are a priori uniformly Dirichlet distributed, and where the primary metric for selecting and discretizing all relevant features is an analytic formula for the probability of error conditioned on the training data. In extending the BDRA for this application, notice that its builtin dimensionality reduction aspects are exploited for isolating and automatically sorting out and mining all points contained in each unknown data cluster. In previous work, this approach was shown to have comparable performance to the classifier that knows all cluster information when mining a single feature containing multiple unknown clusters. Therefore, the primary contribution of the work presented here is to demonstrate that this approach can be extended to cases where the features are fused and contain more than one dimension. To illustrate performance, results are demonstrated using simulated data containing multiple clusters, and where the fused feature space contains relevant classification information.

This chapter provides a survey of some clustering methods relevant to clustering Web elements for better information access. We start with classical methods of cluster analysis that seems to be relevant in approaching the clustering of Web data. Graph clustering is also described since its methods contribute significantly to clustering Web data. The use of artificial neural networks for clustering has the same motivation. Based on previously presented material, the core of the chapter provides an overview of approaches to clustering in the Web environment. Particularly, we focus on clustering Web search results, in which clustering search engines arrange the search results into groups around a common theme. We conclude with some general considerations concerning the justification of so many clustering algorithms and their application in the Web environment.

In this paper we combine clustering ensembles and semisupervised clustering to address the ill-posed nature of clustering. We introduce a mechanism which leverages the ensemble framework to bootstrap informative constraints directly from the data and from the various clusterings, without intervention from the user. Our approach is well suited for problems where the information available from an external source is very limited. We demonstrate the effectiveness of our proposed technique with experiments using real datasets and other state-of-the-art semi-supervised techniques.

Effective client base segmentation is a difficulty for telecom businesses in order to customise services and marketing tactics. In order to obtain precise telecom consumer segmentation, we provide an approach in this paper that blends Principal Component Analysis (PCA) with a Hierarchical K-nearest neighbours (KNN) model. Using PCA, the telecom customer database's dimensionality is decreased, allowing for the capture of the most pertinent data with the least amount of processing overhead. PCA offers a succinct depiction of the database by locating the primary components that account for the majority of the variance in the data. Principal Component Analysis (PCA) enhances database scans in telecom customer segmentation by reducing high dimensionality, thus improving efficiency. By applying PCA, telecom companies can overcome challenges posed by large datasets with numerous features, leading to better quality clustering results. PCA helps in dimensionality reduction, filtering out irrelevant features and noisy data samples, which is crucial for accurate customer segmentation [3]. This reduction in dimensions allows for more efficient database scans, enabling clustering algorithms like K-means to perform better with dimensionally reduced data, ultimately enhancing the accuracy and effectiveness of customer segmentation in the telecom industry. Next, using the neighbouring data points as a basis for classification, the Hierarchical KNN model is used to group consumers in the smaller feature space. This makes it possible to identify discrete client categories with certain traits and inclinations. We illustrate this approach's efficacy in telecom client segmentation with a case study. The outcomes demonstrate the advantages of consumer profiling, cluster analysis, and dimensionality reduction. These information may be used by telecom businesses to create focused marketing campaigns, allocate resources as efficiently as possible, and improve efforts to acquire and retain customers. With the help of this approach, telecom businesses may effectively and pragmatically segment their client base, gaining useful insights from sizable customer datasets to help them make strategic decisions in a cutthroat industry.

Detection and tracking of human face and hands are curial for gesture recognition. In this paper, a skin segmentation framework is presented, where a robust elliptical model in HSV color space is developed, which promises not only to better model the skin color, but also to significantly lower the complexity of the hand detection process. In our approach, hand regions are identified and detected in Hue Saturation Value (HSV) color space that is proven to correspond closer to human perception of hand color than the traditional RGB color space. The obtained results show the potential of the method proposed and it yields good performance and has robustness with respect to illumination as well as with respect to image noise.

We explore the possibility that the G2 gas cloud falling in toward SgrA * is the mass-loss envelope of a young T Tauri star. As the star plunges to smaller radius at 1000-6000 km s -1 , a strong bow shock forms where the stellar wind is impacted by the hot X-ray emitting gas in the vicinity of SgrA * . For a stellar mass-loss rate of 4 × 10 -8 M yr -1 and wind velocity 100 km s -1 , the bow shock will have an emission measure (EM = n 2 vol) at a distance ∼10 16 cm, similar to that inferred from the IR emission lines. The ionization of the dense bow shock gas is potentially provided by collisional ionization at the shock front and cooling radiation (X-ray and UV) from the post shock gas. The former would predict a constant line flux as a function of distance from SgrA * , while the latter will have increasing emission at lesser distances. In this model, the star and its mass-loss wind should survive pericenter passage since the wind is likely launched at 0.2 AU and this is much less than the Roche radius at pericenter (∼3 AU for a stellar mass of 2 M ). In this model, the emission cloud will probably survive pericenter passage, discriminating this scenario from others.

We present a comprehensive comparative analysis of classical Fuzzy C-Means (FCM) clustering and kernel –based Fuzzy C-Means clustering. While Fuzzy C-Means is a popular soft-clustering method, its effectiveness is largely limited to hyper spherical clusters that are linearly separable. If the clusters are not linearly separable, the FCM is not particularly effective. By applying the kernel trick, the kernelised fuzzy c-means algorithm attempts to address this problem. The Kernel FCM operates by first non-linearly mapping the data to appropriate and sufficiently high-dimensional feature spaces, where according to the Mercer’s theorem the data are likely to be linearly separable, and then applying the classical FCM algorithm. We first present the Hard C-Means clustering algorithm and a generalization of it called the Fuzzy C-Means. Then we explore the mathematical basis behind the kernel trick, both in general and especially in the setting of clustering. Following this we evaluate the...

Recent work has focused the incorporation of a priori knowledge into the data clustering process, in the form of pairwise constraints, aiming to improve clustering quality and find appropriate clustering solutions to specific tasks or interests. In this work, we integrate must-link and cannot-link constraints into the cluster ensemble framework. Two algorithms for combining multiple data partitions with instance level constraints are proposed. The first one consists of a modification to Evidence Accumulation Clustering and the second one maximizes both the similarity between the cluster ensemble and the target consensus partition, and constraint satisfaction using a genetic algorithm. Experimental results shown that the proposed constrained clustering combination methods performances are superior to the unconstrained Evidence Accumulation Clustering.

Abstract: The collection of wild larvae seed as a source of raw material is a major sub industry of shellfish aquaculture. To predict when, where and in what quantities wild seed will be available, it is necessary to track the appearance and growth of planktonic larvae. One of the most difficult groups to identify, particularly at the species level are the Bivalvia. This difficulty arises from the fact that fundamentally all bivalve larvae have a similar shape and colour. Identification based on gross morphological appearance is limited by the time- ...

News portals, such as Yahoo News or Google News, collect large amounts of documents from a variety of sources on a daily basis. Only a small portion of these documents can be selected and displayed on the homepage. Thus, there is a strong preference for major, recent events. In this work, we propose a scalable and accurate First Story Detection (FSD) pipeline that identifies fresh news. In comparison to other FSD systems, our method relies on relation extraction methods exploiting entities and their relations. We evaluate our pipeline using two distinct datasets from Yahoo News and Google News. Experimental results demonstrate that our method improves over the state-of-the-art systems on both datasets with constant space and time requirements.

Conceptual clustering is a discovery process that groups a set of data in the way that the intra-cluster similarity is maximized and the inter-cluster similarity is minimized. Traditional clustering algorithms employ some measure of distance between data points in n-dimensional space. However, not all data types can be represented in a metric space, therefore no natural distance function is available for them. We address the problem of clustering sequences of categorical values. We present a measure of similarity for the sequences and an agglomerative hierarchical algorithm that uses frequent sequential patterns found in the database to efficiently generate the resulting clusters. The algorithm iteratively merges smaller, similar clusters into bigger ones until the requested number of clusters is reached.

At present, network security needs to be concerned to provide secure information channels due to increase in potential network attacks. Intrusion Detection System (IDS) is a valuable tool for the defense-in-depth of computer networks. However, building an efficient ID faces a number of challenges. One of the important challenges is dealing with data containing a high number of features. Current IDS examines all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little to the detection process; their usage can decrease the intrusion detection efficiency as well as taking more computational time for the effective response in real time environment. The purpose of this paper is to identify important input features in building IDS that is computationally efficient and effective. In this work we propose the feature selection method by ranking them using the various feature selection algorithms like InfoGain, GainRatio, OneR, RELIEF etc. Combining the features of the best algorithms whose performance is better by comparing the result with each other using J48 classifier. To evaluate the performance of the proposed technique several experiments are conducted on the KDDcup99 dataset for intrusion detection. The empirical results indicate that input features are important to detect the intrusions and reduces the dimensionality of the features, training time and increases overall accuracy.

In this paper, we propose a new method of citation data clustering for author name disambiguation. Most citation data appearing in the reference section of scientific papers include the coauthor first names with their initials. Hence, we often search citation data by using such an abbreviated name, e.g. "S. Lee" or "J. Chen", and consequently obtain many irrelevant data in the search result, because such an abbreviated name refers to many different persons. In this paper, we propose a method of citation data clustering to construct clusters each of which includes only citation data corresponding to a unique author. Our clustering method is based on a probabilistic model which is an extension of the naive Bayes mixture model. Since our model has two hidden variables, we call it two-variable mixture model. In the evaluation experiment, we used the well-known DBLP data set. The results show that the two-variable mixture model can achieve a better balance between precision and recall than the naive Bayes mixture model.

We explore the idea of evidence accumulation for combining the results of multiple clusterings. Initially, n d−dimensional data is decomposed into a large number of compact clusters; the K-means algorithm performs this decomposition, with several clusterings obtained by N random initializations of the K-means. Taking the cooccurrences of pairs of patterns in the same cluster as votes for their association, the data partitions are mapped into a co-association matrix of patterns. This n × n matrix represents a new similarity measure between patterns. The final clusters are obtained by applying a MST-based clustering algorithm on this matrix. Results on both synthetic and real data show the ability of the method to identify arbitrary shaped clusters in multidimensional data.

Each clustering algorithm induces a similarity between given data points, according to the underlying clustering criteria. Given the large number of available clustering techniques, one is faced with the following questions: (a) Which measure of similarity should be used in a given clustering problem? (b) Should the same similarity measure be used throughout the d−dimensional feature space? In other words, are the underlying clusters in given data of similar shape? Our goal is to learn the pairwise similarity between points in order to facilitate a proper partitioning of the data without the a priori knowledge of k, the number of clusters, and of the shape of these clusters. We explore a clustering ensemble approach combined with cluster stability criteria to selectively learn the similarity from a collection of different clustering algorithms with various parameter configurations.

This paper introduces and evaluates a new class of knowledge model, the recursive Bayesian multinet (RBMN), which encodes the joint probability distribution of a given database. RBMNs extend Bayesian networks (BNs) as well as partitional clustering systems. Briefly, a RBMN is a decision tree with component BNs at the leaves. A RBMN is learnt using a greedy, heuristic approach akin to that used by many supervised decision tree learners, but where BNs are learnt at leaves using constructive induction. A key idea is to treat expected data as real data. This allows us to complete the database and to take advantage of a closed form for the marginal likelihood of the expected complete data that factorizes into separate marginal likelihoods for each family (a node and its parents). Our approach is evaluated on synthetic and real-world databases.

The goal of data clustering is to partition a set of data vectors into groups called clusters such that the intra-cluster similarity is maximized while the inter-cluster similarity is minimized. Distributed Clustering is performed on very large data, distributed across machines. Algorithms must operate over distributed data and still produce insightful clusters for the entirety of data. Distributed data-mining needs are growing with data being collected at various sources across the world while their movement and storage at a centralized location may be impossible due to multiple reasons. Often, data is continuously generated and, with time it evolves (a.k.a Concept Drift) to have new clusters while losing the importance of older clusters. Algorithms must operate on these high-dimensional Big-Data streams with limited processing power and memory. Although clustering is unsupervised learning where prior data knowledge is unavailable, most algorithms require parameters. The proper setting for the multiple parameters is challenging to determine. Ideally a solution that operates independent of any user-defined parameters would prove useful. How can we know if something extraordinary or new data trends starts occurring? When proper input parameter values are unknown, the algorithm must be run multiple times while adjusting the parameters to tune the results. It is run for each set of values from a carefully chosen parameter space. Outputs from all runs are evaluated and analyzed using multiple chosen performance metrics. Optimal values of these metrics determine the optimal output for the algorithm. Drawbacks of this include establishing the testing range of values for each parameter, amount of time and computational resources needed to perform this exploration and when it is applied to streaming evolving data, the algorithm execution speed must match the data streaming speed. This dissertation develops a Parameter-Free high-performance approximate method (PPAH) for privacy preserving data stream mining on high-dimensional and distributed data. This algorithm specifically focuses on advancing techniques to locate non-stationary and evolving clusters in a data stream. This approach building on the algorithm RPHash, extends it to operate as a parameter free solution identifying and tracking evolving and nonstationary clusters in the data-stream. RPHash implements privacy preserving data mining that combines random projection, locality sensitive hashing and count-min sketch to perform clustering. We develop a modified and optimized form of the online component of RPHash with a new offline component that extracts multiple candidate clusters from the online data summary. For each of these partitions the online within-cluster sum of squares is computed and sorted for a knee finding algorithm to locate and select cluster prospects for the stream (at that time). Two approaches for data aging to address the concept drift are implemented. Experiments performed on various synthetic and real-world data illustrate the potential utility of the proposed approach. The results show that this algorithm can detect the number of clusters from the data stream at any specific point of time and produce comparable accurate clusters for them, while requiring no input parameters from the user. The run-time and memory remain constant as it runs in batches.

The massive volume of organic information, the conventional software engineering procedures and calculations neglect to take care of complex natural issues of this present reality. In any case, present day computational methodologies, for example, AI can address the restrictions of the customary strategies. AI has assumed a significant job in building up Bioinformatics as a field in its own in the course of the most recent 30 years. For solving the complex problems of biological data we use Machine Learning Techniques for Deoxyribonucleic Acid (DNA) Sequence Alignment data. We present a Systematic Literature Review Protocol (SLRP) of DNA sequence alignment using machine learning technique. This proposition played out an archived arrangement of explicit techniques which are useful to utilize the Systematic Literature Review (SLR). The anticipated outcomes of this survey recognize the DNA Sequence Alignment utilizing Machine Learning Techniques, investigate issues, order the issues, characterize the significant qualities and furthermore talk about the general attributes. The normal advantages of this investigation in future will be Systematic best in class of machine learning method in bioinformatics that will be supportive for new Researchers to represent DNA succession arrangement utilizing machine learning procedures.

The massive volume of organic information, the conventional software engineering procedures and calculations neglect to take care of complex natural issues of this present reality. In any case, present day computational methodologies, for example, AI can address the restrictions of the customary strategies. AI has assumed a significant job in building up Bioinformatics as a field in its own in the course of the most recent 30 years. For solving the complex problems of biological data we use Machine Learning Techniques for Deoxyribonucleic Acid (DNA) Sequence Alignment data. We present a Systematic Literature Review Protocol (SLRP) of DNA sequence alignment using machine learning technique. This proposition played out an archived arrangement of explicit techniques which are useful to utilize the Systematic Literature Review (SLR). The anticipated outcomes of this survey recognize the DNA Sequence Alignment utilizing Machine Learning Techniques, investigate issues, order the issues, characterize the significant qualities and furthermore talk about the general attributes. The normal advantages of this investigation in future will be Systematic best in class of machine learning method in bioinformatics that will be supportive for new Researchers to represent DNA succession arrangement utilizing machine learning procedures.

In recent years, fuzzy based clustering approaches have shown to outperform state-of-the-art hard clustering algorithms in terms of accuracy. The difference between hard clustering and fuzzy clustering is that in hard clustering each data point of the data set belongs to exactly one cluster, and in fuzzy clustering each data point belongs to several clusters that are associated with a certain membership degree. Fuzzy c-means clustering is a well-known and effective algorithm, however, the random initialization of the centroids directs the iterative process to converge to local optimal solutions easily. In order to address this issue a clonal selection based fuzzy c-means algorithm (CSFCM) is introduced. CSFCM is compared with the basic Fuzzy C-Means (FCM) algorithm, a genetic algorithm based FCM (GAFCM) algorithm, and a particle swarm optimization based FCM (PSOFCM) algorithm.

We are developing a multi-channel/multifunction prosthetic hand/arm controller system capable of receiving and processing signals from up to sixteen Implanted MyoElectric Sensors (IMES). The appeal of implanted sensors for myoelectric control is that EMG signals can be measured at their source providing relatively cross-talk free signals that can be treated as independent control sites. Therefore the number of degrees-of-freedom that can be simultaneously controlled and coordinated in an externally-powered prosthesis will be greater than with surface EMG or mechanical control sites. To explore the issue of intra-muscular signal independence and the ability to control them, human subject experiments have been performed in which intra-muscular EMGs were obtained. Choice of muscles was based on a desire to be able to independently control a two degree-of-freedom (DOF) wrist, and 3 DOF prosthetic hand. This paper provide our result so far.

Because of variable dependence, high dimensional data typically have much lower intrinsic dimensionality than the number of its variables. Hence high dimensional data can be expected to lie in (nonlinear) lower dimensional manifold. In this paper, we describe a nonlinear manifold clustering algorithm. By connecting data vectors with their neighbors in feature space, we construct a neighborhood graph from given set data vectors. Furthermore, geometrical invariance, namely dimensionality, are extracted from the neighborhood of vectors, and used to facilitate the clustering procedure. In addition, we discuss a latent model for data cluster descriptions and an EM algorithm to find such descriptions. Preliminary experiments illustrate that this new algorithm can be used to explore the nonlinear structure of data.

We consider the problem of fast time-series data clustering. Building on previous work modeling the correlation-based Hamiltonian of spin variables we present an updated fast non-expensive Agglomerative Likelihood Clustering algorithm (ALC). The method replaces the optimized genetic algorithm based approach (f-SPC) with an agglomerative recursive merging framework inspired by previous work in Econophysics and Community Detection. The method is tested on noisy synthetic correlated time-series data-sets with built-in cluster structure to demonstrate that the algorithm produces meaningful non-trivial results. We apply it to time-series data-sets as large as 20,000 assets and we argue that ALC can reduce compute time costs and resource usage cost for large scale clustering for time-series applications while being serialized, and hence has no obvious parallelization requirement. The algorithm can be an effective choice for state-detection for online learning in a fast non-linear data environment because the algorithm requires no prior information about the number of clusters.

In recent years, thyroid diseases have become increasingly prevalent worldwide. In India, for instance, one in eight women is affected by hypothyroidism, hyperthyroidism, or thyroid cancer. Research indicates that approximately 20% of Indians are diagnosed with endemic goiter. Several factors influence thyroid function, including stress, infection, trauma, toxins, low-calorie diet, and certain medications. Preventing such diseases is crucial as treatments often involve long-term medication or surgical intervention. Thyroid disease diagnosis is pivotal in medicine, where precise classification is essential for prompt treatment. This paper proposes a novel approach to thyroid disease classification by integrating Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) with Association Rule Mining (ARM). The hybridization of PSO and ACO enhances exploration and exploitation capabilities, thereby improving the efficiency of association rule mining for feature selection. The proposed method aims to identify significant association rules among thyroid disease attributes, thereby facilitating accurate classification. Experimental evaluations conducted on benchmark thyroid disease datasets demonstrate the effectiveness of the proposed approach in terms of classification accuracy and computational efficiency. The results indicate that the hybrid PSO and ACO-based association rule mining approach surpasses traditional methods, underscoring its potential to enhance thyroid disease diagnosis systems.

We present a novel extension of watershed cuts to hypergraphs, allowing the clustering of data represented as an hypergraph, in the context of data sciences. Contrarily to the methods in the literature, instances of data are not represented as nodes, but as edges of the hypergraph. The properties associated with each instance are used to define nodes and feature vectors associated to the edges. This rich representation is unexplored and leads to a data clustering algorithm that considers the induced topology and data similarity concomitantly. We illustrate the capabilities of our method considering a dataset of movies, demonstrating that knowledge from mathematical morphology can be used beyond image processing, for the visual analytics of network data. More results, the data, and the source code used in this work are available at .

In recent years, there has been an increasing focus on developing energy-efficient routing algorithms for wireless sensor networks. Due to the limited power resources in sensor nodes, conserving energy is crucial for both individual node longevity and the overall network lifespan. Energy efficiency is a significant concern in wireless sensor networks, particularly due to the high energy consumption associated with transmission tasks, especially over long distances. It has been demonstrated that clustered routing strategies are effective in maximizing network lifespan while minimizing transmission energy. Clustering serves as an efficient data collection technique that aims to reduce energy consumption. In a clustered network, each node sends its gathered data to a specific cluster head. The cluster head then consolidates the data from all member nodes and transmits it to the base station (sink), either in a compressed or uncompressed form. This data transfer occurs in a multi-hop network setting, passing through multiple cluster heads. However, cluster heads near the sink often experience early energy depletion due to the intensive inter-cluster relay. Congestion in network nodes exacerbates issues such as increased packet loss and energy consumption. Routing algorithms must prioritize load balancing among diverse sensor nodes and energy efficiency to enhance network longevity. In response to these challenges, this study introduces a distributed fuzzy-based relay selection mechanism and a K-means clustering algorithm with optimal Cluster Head (CH) selection. The CH selection algorithm employs channel quality and distance to CH characteristics to identify CHs. A distributed fuzzy system, considering link superiority factors, route weight, and node adjacency, is utilized to select relay nodes. The proposed technique undergoes evaluation and comparison with current methods, focusing on metrics such as lifespan, energy consumption, throughput, packet delivery ratio, and end-to-end latency. Simulation results indicate that the proposed strategy outperforms comparable strategies across various parameters.

Wireless sensor networks, also known as WSNs, are made up of small sensor nodes that are able to function and record events in environments that are inaccessible to humans. The data packets that are produced by these networks can be continuous, event-based, or query-driven, and they have a characteristic that is application-specific in character. To avoid wasting any energy, these packets must be transferred to the base station in an effective way. Making ensuring that data packet transport is reliable and energy-efficient is one of the most crucial considerations to make when attempting to enhance the performance of a wireless sensor network (WSN). The conventional method of retransmissions drains the battery of the node and adds to the amount of communication overhead, both of which contribute to network congestion, which in turn hinders the network's ability to transmit
data packets reliably. Congestion results in the depletion of the node's energy supply, a decline in the performance of the network, and an increase in network latency as well as packet loss. Clustering is one of the most effective methods for gathering information from several sensor nodes in a time efficient manner. In this work, we propose an efficient data delivery technique that can help to maximize the network throughput rate without affecting the network performance. In this study, a stable CH selection & link cost function (LCF) based data aggregation approach is developed to increase the throughput & lifetime of a wireless sensor network, CH stability, and energy-efficient data aggregation. The multi-objective parameters such as energy and distance to base station are involved to improve CH stability which improves the data aggregation. Following the selection of the CH, a link cost function (LCF) that is based on relay selection and data aggregation is used in order to find the optimal path between the cluster head and the member nodes for the transmission of data in an efficient manner that uses the least amount of energy. In order to make the most of this time-saving method, the data are consolidated at CHs

A method is introduced for improved estimation of missing data that preserves the multi-regime characteristics of a dataset. The approach analyzes regime change in spatial time series by applying an Expectation-Maximization algorithm (an iterative procedure that finds the Maximum Likelihood Estimate of statistical model parameters) for the determination of a Gaussian Mixture Model (GMM). We estimate the GMM when only a linear noisy measurement of the underlying process is available. We demonstrate the validity of the method using an idealized dataset and also by applying the method to equatorial sea surface salinity observed by the TAO/TRITON array. A percentage of the total observations is systematically extracted and predicted using the method to allow for validation. Finally, the approach is applied to recently available remote sea surface salinity from the SMOS satellite in the Amazon River plume region. Areas of large noise levels (reduced signal-to-noise ratios) are considered as missing data and predicted with the proposed approach. The method interprets regime changes and provides reconstructions of missing information based on the mean and covariability within each regime.

In this paper, we propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakthrough over complex multimodal optimization problems at high dimensions. The first one, which is the so-called multidimensional (MD) PSO, reforms the native structure of swarm particles in such a way that they can make interdimensional passes with a dedicated dimensional PSO process. Therefore, in an MD search space, where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. Among many PSO variants in the literature, none yields a robust solution, particularly over multimodal complex problems at high dimensions. To address this problem, we propose the fractional global best formation (FGBF) technique, which basically collects all the best dimensional components and fractionally creates an artificial global best (aGB) particle that has the potential to be a better "guide" than the PSO's native gbest particle. This way, the potential diversity that is present among the dimensions of swarm particles can be efficiently used within the aGB particle. We investigated both individual and mutual applications of the proposed techniques over the following two well-known domains: 1) nonlinear function minimization and 2) data clustering. An extensive set of experiments shows that in both application domains, MD PSO with FGBF exhibits an impressive speed gain and converges to the global optima at the true dimension regardless of the search space dimension, swarm size, and the complexity of the problem. Index Terms-Fractional global best formation (FGBF), multidimensional (MD) search, particle swarm optimization (PSO). I. INTRODUCTION T HE BEHAVIOR of a single organism in a swarm is often insignificant, but their collective and social behavior is of paramount importance. Particle swarm optimization (PSO) was introduced by Kennedy and Eberhart [27] in 1995 as a population-based stochastic search and optimization process.

This paper presents a new production system architecture that takes advantage of modern associative memory devices to allow parallel production firing, concurrent matching, and overlap among matching, selection, and firing of productions. We prove that the results produced by the architecture are correct according to the serializability Criterion. A comprehensive event driven simulator is used to evaluate the scaling properties of the new architecture and to compare it with a parallel architecture that does global synchronization before every production firing. We also present measures for the improvement in speed due to the use of associative memories and an estimate for the amount of associative memory needed. Architectural evaluation IS facilitated by a new benchmark program that allows for changes in the number of productions, the size of the database, the variance between the sizes of local data clusters, and the ratio between local and global data. Our results indicate that substantial improvements in speed can be achieved with a very modest increase in hardware cost.

Background: Active trachoma is not uniformly distributed in endemic areas, and local environmental factors influencing its prevalence are not yet adequately understood. Determining whether clustering is a consistent phenomenon may help predict likely modes of transmission and help to determine the appropriate level at which to target control interventions. The aims of this study were, therefore, to disentangle the relative importance of clustering at different levels and to assess the respective role of individual, socio-demographic, and environmental factors on active trachoma prevalence among children in Mali. Methodology/Principal Findings: We used anonymous data collected during the Mali national trachoma survey (1996)(1997) at different levels of the traditional social structure (14,627 children under 10 years of age, 6,251 caretakers, 2,269 households, 203 villages). Besides field-collected data, environmental variables were retrieved later from various databases at the village level. Bayesian hierarchical logistic models were fit to these prevalence and exposure data. Clustering revealed significant results at four hierarchical levels. The higher proportion of the variation in the occurrence of active trachoma was attributable to the village level (36.7%), followed by household (25.3%), and child (24.7%) levels. Beyond some wellestablished individual risk factors (age between 3 and 5, dirty face, and flies on the face), we showed that caretaker-level (wiping after body washing), household-level (common ownership of radio, and motorbike), and village-level (presence of a women's association, average monthly maximal temperature and sunshine fraction, average annual mean temperature, presence of rainy days) features were associated with reduced active trachoma prevalence. Conclusions/Significance: This study clearly indicates the importance of directing control efforts both at children with active trachoma as well as those with close contact, and at communities. The results support facial cleanliness and environmental improvements (the SAFE strategy) as population-health initiatives to combat blinding trachoma.