K means algorithm

Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color images segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a wide class of images. However, it is not adequate for noisy images and it takes longer runtimes, as compared to other method like K-means. For this reason, several methods have been proposed to improve these weaknesses. Methods like Fuzzy C-Means with Gustafson-Kessel algorithm (FCM-GK), which improve its performance against the noise, but increase significantly the runtime. In this paper we propose to use the centroids generated by GK-FCM algorithms as seeding for K-means algorithm in order to accelerate the runtime and improve the performance of K-means with random seeding. These segmentation techniques were applied to feature extraction on vineyard images. Segmented images were evaluated using several quality parameters such as the rate of correctly classified area and runtime.

This survey covers some very recent applications of data miningtechniques in the field of agriculture. This is an emerging researchfield that is experiencing a constant development. In this paper, we firstpresent two applications in this field in details; in particular, we considerthe problem of discovering problematic wine augmentations at the earlystages of the process, and the problem of predicting yield production byusing sensor data information. Secondly, we briefly describe other problemsin the field for which we found very recent contributions in the Scientificliterature.

The idea of evidence accumulation for the combination of multiple clusterings was recently proposed . Taking the K-means as the basic algorithm for the decomposition of data into a large number, k, of compact clusters, evidence on pattern association is accumulated, by a voting mechanism, over multiple clusterings obtained by random initializations of the K-means algorithm. This produces a mapping of the clusterings into a new similarity measure between patterns. The final data partition is obtained by applying the single-link method over this similarity matrix. In this paper we further explore and extend this idea, by proposing: (a) the combination of multiple K-means clusterings using variable k; (b) using cluster lifetime as the criterion for extracting the final clusters; and (c) the adaptation of this approach to string patterns. This leads to a more robust clustering technique, with fewer design parameters than the previous approach and potential applications in a wider range of problems.

Available in: http://www.redalyc.org/src/inicio/ArtPdfRed.jsp?iCve=43019328016 ... Redalyc Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and ... A harmony search algorithm for clustering with feature selection ...

Short term electricity load forecasting is nowadays, of paramount importance in order to estimate next day electricity load resulting in energy save and environment protection. Electricity demand is influenced (among other things) by the day of the week, the time of year and special periods and/or days such as Ramadhan, all of which must be identified prior to modeling. This identification, known as day-type identification, must be included in the modeling stage either by segmenting the data and modeling each day-type separately or by including the day-type as an input. This paper investigates day-type identification approach for Algerian electricity load. Kohonen maps are used to identify daytypes. The K-Means clustering method will be used as a complementary method to precisely identify the obtained classes. Clustering validity is done by using a criteria measurement of quality. This work has allowed the identification of six different classes.

Our ability to demonstrate statistical patterns of invasion by non-native species will determine the success of future management projects. We investigated the suitability of self-organizing maps (SOM, neural network) for patterning habitat invasion by exotic fish species at the regional scale (Southwest France), using a binary dataset of species occurrences. The SOM visualization can be used as an analytical tool to bring out relationships between sample locations and biological variables, but in addition the weight of each species in the output of the SOM can be interpreted as its occurrence probability in various geographic areas. After training the SOM with fish presence/absence data, the k-means algorithm helped to derive three major clusters of sites (headwater, montane, and plain areas). Each cluster was divided into two subsets of sites according to non-native fish, because assemblage compositions delineated different geological areas: Pyrenees Mountains, Massif Central Moun...

We describe the use of a binary hierarchical clustering (BHC) framework for clustering of gene expression data. The BHC algorithm involves two major steps. Firstly, the K-means algorithm is used to split the data into two classes. Secondly, the Fisher criterion is applied to the classes to assess whether the splitting is acceptable. The algorithm is applied to the sub-classes recursively and ends when all clusters cannot be split any further. BHC does not require the number of clusters to be known. It does not place any assumption about the number of samples in each cluster or the class distribution. The hierarchical framework naturally leads to a tree structure representation. We show that by arranging the BHC clustered gene expression data in a tree structure, we can easily visualize the cluster results. In addition, the tree structure display allows user judgement in finalizing the clustering result using prior biological knowledge.

Most of the earlier work on clustering has mainly been focused on numerical data whose inherent geometric properties can be exploited to naturally define distance functions between data points. Recently, the problem of clustering categorical data has started drawing interest. However, the computational cost makes most of the previous algorithms unacceptable for clustering very large databases. The k-means algorithm is well known for its efficiency in this respect. At the same time, working only on numerical data prohibits them from being used for clustering categorical data. The main contribution of this paper is to show how to apply the notion of "cluster centers" on a dataset of categorical objects and how to use this notion for formulating the clustering problem of categorical objects as a partitioning problem. Finally, a k-means-like algorithm for clustering categorical data is introduced. The clustering performance of the algorithm is demonstrated with two well-known data sets, namely, soybean disease and nursery databases.

We discuss types of clustering problems where error information associated with the data to be clustered is readily available and where error-based clustering is likely to be superior to clustering methods that ignore error. We focus on clustering derived data (typically parameter ...

Kernel k-means is an extension of the standard kmeans clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, in this work we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters and, due to its incremental nature and search procedure, locates near optimal solutions avoiding poor local minima. Furthermore a modification is proposed to reduce the computational cost that does not significantly affect the solution quality. We test the proposed methods on artificial data and also for the first time we employ kernel k-means for MRI segmentation along with a novel kernel. The proposed methods compare favorably to kernel k-means with random restarts.

A multi-channel wireless EEG (electroencephalogram) acquisition and recording system is developed in this work. The system includes an EEG sensing and transmission unit and a digital processing circuit. The former is composed of pre-amplifiers, filters, and gain amplifiers. The kernel of the later digital processing circuit is a micro-controller unit (MCU, TI-MSP430), which is utilized to convert the EEG signals into digital signals and fulfill the digital filtering. By means of Bluetooth communication module, the digitized signals are sent to the back-end such as PC or PDA. Thus, the patient's EEG signal can be observed and stored without any long cables such that the analogue distortion caused by long distance transmission can be reduced significantly. Furthermore, an integrated classification method, consisting of non-linear energy operator (NLEO), autoregressive (AR) model, and bisecting k-means algorithm, is also proposed to perform EEG off-line clustering at the back-end. ...

Energy efficiency is the most critical challenge in wireless sensor network. The transmission energy is the most consuming task in sensor nodes, specifically in large distances. Clustered routing techniques are efficient approaches used to lower the transmission energy and maximize the network's lifetime. In this paper, a hybrid clustered routing approach is proposed for energy optimization in WSN. This approach is based on K-Means clustering algorithm and LEACH protocol. The simulation results using MATLAB tool have shown that the proposed hybrid approach outperforms LEACH protocol and optimizes the nodes energy and the network lifetime. 1. INTRODUCTION Wireless sensors network (WSN) is composed of group of wireless sensors deployed in field with a high density; enables detection of physical phenomena such as thermal, optical, vibration and temperature or pressure [1], [2]. WSN are being increasingly used for several application areas, such as environmental monitoring, military target tracking, healthcare, seismic sensing, etc. Each sensor node is assembled with a transducer, microcomputer, transceiver and power source. These wireless nodes are usually powered by limited capacity batteries which replacement is delicate in hostile environment where hundreds of them are randomly deployed. The major limitations encountered in WSN are energy resources and high cost of transmission [3], [4]. Therefore, energy limitation is the key challenges in wireless sensors network, since the batteries are not rechargeable usually which limits the network lifetime [5-7]. To extend the wireless network lifetime, the energy of sensor nodes should be efficiently used. Energy required in communications is the important consuming part compared to processing process in sensor nodes [8]. A variety of techniques have been developed in order to reduce the communication energy consumption. Optimized routing techniques such as clustering algorithms have been widely adopted in WSN due to their energy effectiveness. This paper investigates the energy efficiency of clustering in routing protocol. We propose a substantial K-Means clustering algorithm based on LEACH protocol which consists of using jointly K-Means and LEACH approaches. The proposed hybrid algorithm is proved to be energy efficient than LEACH protocol and significantly extends the sensor network lifetime. The remainder of this paper is organized as follows. Section 2 discusses the outline of K-Means algorithm. The improved K-Means algorithm is described in section 3. Simulations and results are discussed in section 4. The final section concludes the paper by setting the direction of future work.

Customer churn is a significant issue that is regularly related with the existence cycle of the business. At the point when the business is in a development period of its life cycle, deals are expanding exponentially and the quantity of new clients to a great extent dwarfs the quantity of churners. On the other side, organizations in a develop period of in their life cycle, set their attention on lessening the rate of customer churn. This research work proposes an efficient computational intelligence model comprising of clustering achieved through improvised K-Means algorithm and classification achieved through Non Linear Support Vector Machine.

Clustering is a division of data into groups of similar objects. K-means has been used in many clustering work because of the ease of the algorithm. Our main effort is to parallelize the k-means clustering algorithm. The parallel version is implemented based on the inherent parallelism during the Distance Calculation and Centroid Update phases. The parallel K-means algorithm is designed in such a way that each P participating node is responsible for handling n/P data points. We run the program on a Linux Cluster with a maximum of eight nodes using message-passing programming model. We examined the performance based on the percentage of correct answers and its speed-up performance. The outcome shows that our parallel K-means program performs relatively well on large datasets.

In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. The problem is known to have local minimum solutions which are usually what the K-means algorithm obtains. The simulated annealing approach for solving optimization problems is described and is proposed for solving the clustering problem. The parameters of the algorithm are discussed in detail and it is shown that the algorithm converges to a global solution of the clustering problem. We also find optimal parameters values for a specific class of data sets and give recommendations on the choice of parameters for general data sets. Finally, advantages and disadvantages of the approach are presented.

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed N-K means clustering algorithm over existing K-means clustering algorithm in terms of complexity and overall performance.

The fast evolution of digital video has brought many new multimedia applications and, as a consequence, has increased the amount of research into new technologies that aim at improving the effectiveness and efficiency of video acquisition, archiving, cataloging and indexing, as well as increasing the usability of stored videos. Among possible research areas, video summarization is an important topic that potentially enables faster browsing of large video collections and also more efficient content indexing and access. Essentially, this research area consists of automatically generating a short summary of a video, which can either be a static summary or a dynamic summary. In this paper, we present VSUMM, a methodology for the production of static video summaries. The method is based on color feature extraction from video frames and k-means clustering algorithm. As an additional contribution, we also develop a novel approach for the evaluation of video static summaries. In this evaluation methodology, video summaries are manually created by users. Then, several user-created summaries are compared both to our approach and also to a number of different techniques in the literature. Experimental results show -with a confidence level of 98% -that the proposed solution provided static video summaries with superior quality relative to the approaches to which it was compared.

Data mining in educational field is a major application of data mining, it use machine learning to learn from data by studying algorithms and their constructions. In Data mining, clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. There are too many algorithms for clustering technique but k-mean algorithm is easy to interpret and understand so, in this paper clustering K-mean algorithm is discussed and applied on students data sets to find the gender wise performance in theory and practical subjects of computer science course. K-mean algorithm is implemented on student’s dataset using WEKA tool.

The huge amount of healthcare data, coupled with the need for data analysis tools has made data mining interesting research areas. Data mining tools and techniques help to discover and understand hidden patterns in a dataset which may not be possible by mainly visualization of the data. Selecting appropriate clustering method and optimal number of clusters in healthcare data can be confusing and difficult most times. Presently, a large number of clustering algorithms are available for clustering healthcare data, but it is very difficult for people with little knowledge of data mining to choose suitable clustering algorithms. This paper aims to analyze clustering techniques using healthcare dataset, in order to determine suitable algorithms which can bring the optimized group clusters. Performances of two clustering algorithms (K-means and DBSCAN) were compared using Silhouette score values. Firstly, we analyzed K-means algorithm using different number of clusters (K) and different distance metrics. Secondly, we analyzed DBSCAN algorithm using different minimum number of points required to form a cluster (minPts) and different distance metrics. The experimental result indicates that both K-means and DBSCAN algorithms have strong intra-cluster cohesion and inter-cluster separation. Based on the analysis, K-means algorithm performed better compare to DBSCAN algorithm in terms of clustering accuracy and execution time.

This paper develops a modularization scheme based on the functional model of a system. The modularization approach makes use of the function-behavior-state (FBS) model of the system to derive the entity relations. The design structure matrix (DSM) is automatically constructed based on the FBS model. In this way, the tedious work of filling the DSM entries based on expert knowledge is avoided. The approach makes use of k-means clustering algorithm to allow the user to try different number of clusters in a fast way. The k-means clustering is adopted for DSM based modularization by defining a proper entity representation, relation measure and objective function. Two modularization schemes are performed, one based on the immediate relations and one on the deeper behavioral relations between the components. Considering the application on the shifting system of the Delft University of Technology (DUT) Formula Student car, the latter modularization resulted in more mechatronic behavior based modules, while the former resulted in modules based on mere disciplinary and spatial closeness.

The K-means algorithm is a commonly used technique in cluster analysis. In this paper, several questions about the algorithm are addressed. The clustering problem is first cast as a nonconvex mathematical program. Then, a rigorous proof of the finite convergence of the K-means-type algorithm is given for any metric. It is shown that under certain conditions the algorithm may fail to converge to a local minimum, and that it converges under differentiability conditions to a Kuhn-Tucker point. Finally, a method for obtaining a local-minimum solution is given. Index Terms-Basic ISODATA, cluster analysis, K-means algorithm, K-means convergence, numerical taxonomy.

Crime data analysts can assist law enforcement officers in accelerating the crime prediction process with the rising advent of computerized systems. This survey paper, therefore, presents a description of the tools and strategies used in the study and forecast of crime data. Crime forecasts are a way to exploit and reduce future crime by anticipating when future crime will happen. Crime prediction relates historical data and predicts the next crime, based on place, time, day, season, and year after analyzing data.This paper provides a fair review of numerous discoveries and predictions of potential crime

With the increase of volume, velocity, and variety of big data, the traditional collaborative filtering recommendation algorithm, which recommends the items based on the ratings from those like-minded users, becomes more and more inefficient. In this paper, two varieties of algorithms for col-laborative filtering recommendation system are proposed. The first one uses the improved k-means clustering technique while the second one uses the improved k-means clustering technique coupled with Principal Component Analysis as a dimensionality reduction method to enhance the recommendation accuracy for big data. The experimental results show that the proposed algorithms have better recommendation performance than the traditional collaborative filtering recommendation algorithm.

Telecommunications companies collect and store enormous amounts of data, including call detail data, network data, and customer data. Nowadays, most telecom companies with poor clustering have a difficult time delivering the exact product or service to satisfy the needs of their customers.
In this research work, the KDD methodology is followed to conduct the data mining process. The mobile subscribers' CDR data along with billing information and customer category are collected, cleansed, transformed, and integrated for experimenting with the clustering data mining method. Various experiments are conducted using the MATLAB data mining tool.
In this thesis, two clustering algorithms, K-means and Fuzzy C-Means clustering algorithm were used to cluster customers using real CDR datasets from an ethio telecom mobile company. Based on the Silhouette evaluation criterion, the optimal number of clusters was three. We have been two major experimental phases in this research work. In the first experiment phase, we have used normal K-means clustering and Fuzzy C-means clustering techniques. In the second experimental phase, we have used the normalization process before doing the K-means clustering and a Fuzzy C-means clustering techniques on the data set.  Finally, using a normalization method procedure, we were able to obtain a satisfactory result.
The valued cluster was found using the K-means clustering algorithm and the Fuzzy C-means clustering algorithm. Hence, these clusters are compared with the ground truth label data set, the k-means clustering algorithm was 99.6% accurate in clustering telecom customers, while the Fuzzy C-means clustering algorithm was 99.1% accurate in clustering telecom customers.
The K-Means clustering algorithm has a high purity value and a low entropy value. Good clustering confirms this. Whereas, compared to K-Means, the Fuzzy C-Means clustering have a lower purity value and a high entropy value. K-means clustering is more accurate and takes less time to compute. Fuzzy C-means clustering shows similar results that are comparable to K-means clustering, but it takes longer to compute. Therefore, the K-means clustering algorithm was found to be more appropriate for clustering telecom users.
Key Words: Clustering, Data mining, MATLAB tool, K-means algorithm, Fuzzy C-means algorithm, and KDD process.

In archaeological applications involving the spatial clustering of two-dimensional spatial data k-means cluster analysis has proved to be a popular method for over 40 years. There are alternatives to k-means analysis which can be thought of as either ‘competitive’ or ‘complementary’ such as k-medoids, model-based clustering, fuzzy clustering and density based clustering among many possibilities. Most of these have been little used in archaeology. That k-means has been a method of choice is probably because it is easily understood, is perceived as being geared to archaeological needs, and was rendered accessible at a time when computational resources were
limited compared to what is now available. It is, in fact, a long-established approach to clustering that pre-dates archaeological interest in it by some years. The theses of the present paper are that
(a) other methods are available that potentially improve on what is possible with k-means;
(b) these are (mostly) as readily understood as k-means;
(c) they are now as easy to implement as k-means is; and
(d) merit more attention than they have received from practitioners who find k-means useful.
The arguments are illustrated by extensive application to a data set that has been the subject of several previous studies.

Health is a valuable thing for humans because anyone can experience health problems, as well as in humans are very susceptible to various diseases but the cause we do not realize. The K-means algorithm is not affected by the order of the objects used, it is proved when the author tries to randomly determine the starting point of the cluster center of one of the objects at the beginning of the calculation. The number of cluster members generated amounts to the same when using other objects as the starting point of the cluster center. However, this only affects the number of iterations performed. Object grouping (object clustering) is one of the processes of object mining that aims to partition an existing object into one or more cluster objects based on its characteristics. This study examines how the use of K-means Cluster Analysis method in the case study of human contagious diseases on an object. This study examines the K-means Cluster Analysis method in infectious diseases in humans based on the set of variables established per sub-district of each Puskesmas in which there are 32 Puskesmas offices in Majalengka District. Abstrak Kesehatan merupakan hal yang berharga bagi manusia karena siapa saja dapat mengalami gangguan kesehatan, begitu pula pada manusia yang sangat rentan terhadap berbagai macam penyakit namun penyebabnya tidak kita sadari. Algoritma K-means tidak terpengaruh terhadap urutan objek yang digunakan, hal ini dibuktikan ketika penulis mencoba menentukan secara acak titik awal pusat cluster dari salah satu objek pada permulaan perhitungan. Jumlah keanggotaan cluster yang dihasilkan berjumlah sama ketika menggunakan objek yang lain sebagai titik awal pusat cluster tersebut. Namun, hal ini hanya berpengaruh pada jumlah iterasi yang dilakukan. Pengelompokan objek (objek clustering) adalah salah satu proses dari objek mining yang bertujuan untuk mempartisi objek yang ada kedalam satu atau lebih cluster objek berdasarkan karakteristiknya. Penelitian ini mengkaji bagaimana penggunaan Algoritma K-means Cluster Analysis dalam studi kasus penyakit menular manusia pada suatu objek. Penelitian ini mengkaji metode K-means Cluster Analysis dalam penyakit menular pada manusia berdasarkan set variabel yang dibentuk per kecamatan tiap Puskesmas yang jumlahnya ada 32 Kantor Puskesmas di Kabupaten Majalengka.

Data Mining is a process of extracting useful information
from a large dataset and Clustering is one of important
technique in data mining process, whose main purpose is to
group data of similar types into clusters and finding a
structure among unlabelled data. In this paper we have
taken four different clustering algorithm i.e. K-Mean
algorithm, Hierarchical algorithm, Density based algorithm,
EM algorithm. All these algorithms are applied on data of
egit software repositories and depends or dependent classes.
In this paper to compare and analyze these four algorithms
with respect to time to build a model, cluster instances,
squared errors and log likelihood by using Weka tool.

viii ix x The IADIS European Conference on Data Mining 2009 received 63 submissions from more than 19 countries. Each submission has been anonymously reviewed by an average of five independent reviewers, to ensure that accepted submissions were of a high standard. Consequently only 14 full papers were published which means an acceptance rate of about 22 %.

The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas. Since internet, social network, and big data grow rapidly, multi-view data become more important. For analyzing multi-view data, various multi-view k-means clustering algorithms have been studied. However, most of multi-view k-means clustering algorithms in the literature cannot give feature reduction during clustering procedures. In general, there often exist irrelevant feature components in multi-view data sets that may cause bad performance for these clustering algorithms. There also exists high feature dimension in multi-view data sets so it is necessary to consider reducing its dimension for clustering algorithms. In this paper, a learning mechanism for the multi-view k-means algorithm to automatically compute individual feature weight is constructed. It can reduce these irrelevant feature components in each view. A new multi-view k-means objective function is firstly proposed for constructing the learning mechanism for feature weights in multi-view clustering. A schema for eliminating irrelevant feature(s) with small weight(s) is then considered for feature reduction. Therefore, a new type of multi-view k-means, called a feature-reduction multi-view k-means (FRMVK), is proposed. The computational complexity of FRMVK is also analyzed. Numerical and real data sets are used to compare FRMVK with other feature-weighted multi-view k-means algorithms. Experimental results and comparisons actually demonstrate the effectiveness and usefulness of the proposed FRMVK clustering algorithm.

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used.

In eye authentication process, the pupil detection is most crucial step to recognize the eye. In eye, iris and sclera are used as the previous inputs using to recognize the eye with different mechanisms like segmentation combining with different versions. The inner edge in the eye is not a normal circle, which may create problem in accurate recognition. In segmentation process, if the image is having less texture then it leads to iris legacy. In this paper, we concentrate only on pupil to recognize the eye. To find the edges in the image we propose canny edge detection method, to reduce the noisy data in the image and detecting the edges. After detecting edges, those images are stored in CASIA database. Secondly, the K-means method is to identify the nearest pupil edge images from the database for the given input image. The results show that identifying pupil is better method to recognizing the eye and raising the recognition accuracy.

One of the most promising approaches for clustering is based on methods of mathematical programming. In this paper we propose new optimization methods based on DC (Difference of Convex functions) programming for hierarchical clustering. A bilevel hierarchical clustering model is considered with different optimization formulations. They are all nonconvex, nonsmooth optimization problems for which we investigate attractive DC optimization Algorithms called DCA. Numerical results on some artificial and real-world databases are reported. The results demonstrate that the proposed algorithms are more efficient than related existing methods.

In our information societies, we increasingly delegate tasks and decisions to automated systems, devices and agents that mediate human relationships, by taking decisions and acting on the basis of algorithms. Their increased intelligence, autonomous behavior and connectivity are changing crucially the life conditions of human beings as well as altering traditional concepts and ways of understanding reality. Algorithms are directed to solve problems that are not always detectable in their own relevance and timeliness. They are also meant to solve those problems through procedures that are not always visible and assessable in their own. In addition, technologies based on algorithmic procedures more and more infer personal information from aggregated data, thus profiling human beings and anticipating their expectations, views and behaviors. This may have normative, if not discriminatory, consequences. While algorithmic procedures and applications are meant to serve human needs, they risk to create an environment in which human beings tend to develop adaptive strategies by conforming their behaviour to the expected output of the procedures, with serious distortive effects. Against this backdrop, little room is often left for a process of rational argumentation able to challenge the results of algorithmic procedures by putting into question some of their hidden assumptions or by taking into account some neglected aspects of the problems under consideration. At the same time, it is widely recognized that scientific and social advances crucially depend on such an open and free critical discussion.

Image retrieval is still an active research topic in the computer vision field. There are existing several techniques to retrieve visual data from large databases. Bag-of-Visual Word (BoVW) is a visual feature descriptor that can be used successfully in Content-based Image Retrieval (CBIR) applications. In this paper, we present an image retrieval system that uses local feature descriptors and BoVW model to retrieve efficiently and accurately similar images from standard databases. The proposed system uses SIFT and SURF techniques as local descriptors to produce image signatures that are invariant to rotation and scale. As well as, it uses K-Means as a clustering algorithm to build visual vocabulary for the features descriptors that obtained of local descriptors techniques. To efficiently retrieve much more images relevant to the query,
SVM algorithm is used. The performance of the proposed system is evaluated by calculating both precision and recall. The
experimental results reveal that this system performs well on two different standard datasets.

To enhance the quality of education system, student performance analysis plays an important role for decision support. Evaluation of student's performance is an important aspect in every institution. The student's knowledge about the programming concepts can be tested using various forms of assessments. It helps the teacher to identify students who need special attention and allow the teacher to provide appropriate guidance. To achieve this a machine learning approach called the Recursive Clustering technique is used to group the students of the programming course into groups based on their performance in the given assessment. There are three groups: Pass, Fail and Above Average. More attention is given to the students present in lower group as they are prone to fail. Study materials are provided to the students for preparation. Tests are conducted for the students who fail. The process is repeated for three times so that most of the students from the lower group will move to the higher group. The results are compared using k-means algorithm. The results prove that this approach provides an effective way to predict the low performing students from their assessment, thereby enabling the student to be on track.

Tugas Akhir merupakan tahap akhir yang harus dilakukan oleh setiap mahasiswa yang akan menyelesaikan masa studinya di kampus. Setiap tahun banyak mahasiswa yang mengajukan judul tugas akhir mereka. . Akan tetapi tidak sedikit dari mahasiswa yang melakukan tindakan jalan pintas yaitu dengan cara plagiat untuk menuntaskan tugas akhir mereka. Oleh karena itu dibutuhkannya sebuah solusi untuk dapat mendeteksi adanya tindak plagiasi dalam proses pengajuan tugas akhir. Untuk mengatasi masalah tersebut penelitian ini dilakukan proses text mining menggunakan sistem klasifikasi dan pencarian tugas akhir dengan menggunakan  metode K – Means dan Vector Space Model. Tahapan untuk proses text mining adalah text processing dengan parsing, text transformation dengan stemming dan stopword removal, feature selection dan pattern discovery.

The k-means algorithm is a partitional clustering method. Over 60 years old, it has been successfully used for a variety of problems. The popularity of k-means is in large part a consequence of its simplicity and efficiency. In this paper we are inspired by these appealing properties of k-means in the development of a clustering algorithm which accepts the notion of "positively" and "negatively" labelled data. The goal is to discover the cluster structure of both positive and negative data in a manner which allows for the discrimination between the two sets. The usefulness of this idea is demonstrated practically on the problem of face recognition, where the task of learning the scope of a person's appearance should be done in a manner which allows this face to be differentiated from others.

In this paper an attempt has been made to review the research studies on application of data mining techniques in the field of agriculture. Some of the techniques, such asID3 algorithms, the k-means, the k nearest neighbour, artificial neural networks and support vector machines applied in the field of agriculture were presented. Data mining in application in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. This article explores the applications of data mining techniques in the field of agriculture and allied sciences.

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-ICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering.