Comparison between Cluster Analysis Algorithms

Authors

  • Ved Prakash Jha1, Soumya Mishra2, Niharika Singh3, Swati Vashisht4 Department of Computer Engineering, AMITY University Gr. Noida Campus, Knowledge Park-3, Uttar Pradesh, India

Abstract

Due to explosions in the number of autonomous data sources there is a growing need for effective approaches to distributive data clustering. Clustering is the process of grouping of data by finding similarities between data based on their characteristics. These groups are called Clusters.[4] Each cluster consists of objects that are similar between themselves and dissimilar compared to objects of other groups. In this place we are going to compare and analyse the performance of different clustering algorithm. All the algorithms are compared according to the number of clusters, size of dataset, type of dataset, and type of software used. We extract the conclusion based on performance, quality and accuracy of the clustering algorithm.
Keywords: Clustering, Clustering Algorithms, K-Means Algorithm, Hierarchical clustering algorithm, Self-organizing maps (SOM) Algorithm, Expectation Maximization Clustering Algorithm (EM).

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Published

2015-02-27

How to Cite

Niharika Singh3, Swati Vashisht4, V. P. J. S. M. (2015). Comparison between Cluster Analysis Algorithms. International Journal of Engineering Technology and Computer Research, 3(1). Retrieved from http://ijetcr.org/index.php/ijetcr/article/view/111

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