Unsupervised distributed clustering

TJPRC, 2013

Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. The central issue is to propose a new data mining algorithm that results better cluster configuration than previous algorithms. The issue of determining the most appropriate cluster configuration is a challenging one, and is addressed in this paper.

Lecture Notes in Computer Science, 2003

Clustering can be defined as the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups (clusters). There is a growing need for parallel algorithms in this field since databases of huge size are common nowadays. This paper presents a parallel version of a recently proposed algorithm that has the ability to scale very well in parallel environments.

Categorizing the different types of data over network is still an important research issue in the field of distributed clustering. There are different types of data such as news, social networks, and education etc. All this text data available in different resources. In searching process the server have to gather information about the keyword from different resources. Due to more scalability this process leads more burdens to resources. So we introduced a framework that consists of efficient grouping method and efficiently clusters the text in the form of documents. It guarantees that more text documents are to be clustered faster.

intechopen.com

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2015

Distributed data mining techniques and mainly distributed clustering are widely used in last decade because they deal with very large and heterogeneous datasets which cannot be gathered centrally. Current distributed clustering approaches are normally generating global models by aggregating local results that are obtained on each site. While this approach analyses the datasets on their locations the aggregation phase is complex, time consuming and may produce incorrect and ambiguous global clusters and therefore incorrect knowledge. In this paper we propose a new clustering approach for very large spatial datasets that are heterogeneous and distributed. The approach is based on K-means Algorithm but it generates the number of global clusters dynamically. It is not necessary to fix the number of clusters. Moreover, this approach uses a very sophisticated aggregation phase. The aggregation phase is designed in such away that the final clusters are compact and accurate while the overall process is efficient in time and memory allocation. Preliminary results show that the proposed approach scales up well in terms of running time, and result quality, we also compared it to two other clustering algorithms BIRCH and CURE and we show clearly this approach is much more efficient than the two algorithms.

Communications in Computer and Information Science, 2018

The analysis of big data requires powerful, scalable, and accurate data analytics techniques that the traditional data mining and machine learning do not have as a whole. Therefore, new data analytics frameworks are needed to deal with the big data challenges such as volumes, velocity, veracity, variety of the data. Distributed data mining constitutes a promising approach for big data sets, as they are usually produced in distributed locations, and processing them on their local sites will reduce significantly the response times, communications, etc. In this paper, we propose to study the performance of a distributed clustering, called Dynamic Distributed Clustering (DDC). DDC has the ability to remotely generate clusters and then aggregate them using an efficient aggregation algorithm. The technique is developed for spatial datasets. We evaluated the DDC using two types of communications (synchronous and asynchronous), and tested using various load distributions. The experimental results show that the approach has super-linear speed-up, scales up very well, and can take advantage of the recent programming models, such as MapReduce model, as its results are not affected by the types of communications.

Studies in Fuzziness and Soft Computing, 2005

The extraction of meaningful information from large collections of data is a fundamental issues in science. To this end, clustering algorithms are typically employed to identify groups (clusters) of similar objects. A critical issue for any clustering algorithm is the determination of the number of clusters present in a dataset. In this contribution we present a clustering algorithm that in addition to partitioning the data into clusters, it approximates the number of clusters during its execution. We further present modifications of this algorithm for different distributed environments, and dynamic databases. Finally, we present a modification of the algorithm that exploits the fractal dimension of the data to partition the dataset.

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016

Clustering techniques are very attractive for extracting and identifying patterns in datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality data, heterogeneity, and high complexity of some algorithms. For instance, some algorithms may have linear complexity but they require the domain knowledge in order to determine their input parameters. Distributed clustering techniques constitute a very good alternative to the big data challenges (e.g.,Volume, Variety, Veracity, and Velocity). Usually these techniques consist of two phases. The first phase generates local models or patterns and the second one tends to aggregate the local results to obtain global models. While the first phase can be executed in parallel on each site and, therefore, efficient, the aggregation phase is complex, time consuming and may produce incorrect and ambiguous global clusters and therefore incorrect models. In this paper we propose a new distributed clustering approach to deal efficiently with both phases; generation of local results and generation of global models by aggregation. For the first phase, our approach is capable of analysing the datasets located in each site using different clustering techniques. The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in time and memory allocation. For the evaluation, we use two well-known clustering algorithms; K-Means and DBSCAN. One of the key outputs of this distributed clustering technique is that the number of global clusters is dynamic; no need to be fixed in advance. Experimental results show that the approach is scalable and produces high quality results.

Lecture Notes in Computer Science, 2007

Many parallel and distributed clustering algorithms have already been proposed. Most of them are based on the aggregation of local models according to some collected local statistics. In this paper, we propose a lightweight distributed clustering algorithm based on minimum variance increases criterion which requires a very limited communication overhead. We also introduce the notion of distributed perturbation to improve the globally generated clustering. We show that this algorithm improves the quality of the overall clustering and manage to find the real structure and number of clusters of the global dataset.

— For business and real time applications large set of data sets are used to extract the unknown patterns which is termed as data mining approach. Clustering and classification algorithm are used to classify the unlabeled data from the large data set in a supervised and unsupervised manner. The assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in any way is termed as cluster analysis or clustering. Through these algorithms the inferences of the clustering process and its domain application competence is determined.the research work deals with K-means and MCL algorithms which are most delegated clustering algorithms.