Overview of Different Data Clustering Algorithms for Static and Dynamic Data Sets

International Journal of Computer Science and Engineering

© 2018 by SSRG - IJCSE Journal

Volume 5 Issue 3

Year of Publication : 2018

Authors : Johnsymol Joy

10.14445/23488387/IJCSE-V5I3P101

How to Cite?

Johnsymol Joy, "Overview of Different Data Clustering Algorithms for Static and Dynamic Data Sets," SSRG International Journal of Computer Science and Engineering , vol. 5,  no. 3, pp. 1-3, 2018. Crossref, https://doi.org/10.14445/23488387/IJCSE-V5I3P101

Abstract:

Data mining is the process of extracting meaningful information from a large set of data. Data clustering is one of the major techniques used in data mining. These techniques will group related data in to identical groups. Data clustering is an unsupervised data analysis and data mining technique; it generates meaningful views from an inherent structure of data. Hundreds of clustering algorithms have been developed by researchers from a number of different scientific disciplines. Data may be static or dynamic. This paper focussed on different clustering algorithms for static and dynamic datasets.

Keywords:

Data mining, data clustering, data stream, Bayesian classifier, decision tree, Pattern mining etc

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