Applications of Cluster Analysis

Authors

  • T Aruna Dept. of Computer Applications, Islamiah Women’s Arts and Science College

Keywords:

Cluster, Matrix, Algorithm

Abstract

Clustering is the process of grouping the data into classes or clusters, so those objects with in a cluster have high similarity in comparison to one another but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on attribute values describing the objects. Clustering has its roots in many areas like data mining, statistics, biology and machine learning. We examine several clustering techniques organized into the following categories partitioning methods, hierarchical method, density based method, grid- based method, model-based method, frequent pattern based method and constraint clustering.

References

[1] L. Jawadeekar, “ Data Mining concepts and Techniques”, Tata McGraw-Hill Publication, India,

[2] Jiawei han, Micheline Kamber, Jian pei “ Data Mining concepts and Techniques”, Publisher: Morgan Kaufman,

[3] Hand Amber pei i “ Data Mining concepts and Techniques”, Second Edition

[4] S. Mythili, E.Madhaiya,“An Analysis on Clustering Algorithms in Data mining”, IJCSMC, Vol. 3, Issue. 1, January 2014, pg.334 – 340.

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Published

2025-11-25

How to Cite

[1]
T. Aruna, “Applications of Cluster Analysis”, Int. J. Comp. Sci. Eng., vol. 7, no. 17, pp. 19–21, Nov. 2025.

Issue

Vol. 7 No. 17 (2019): IJCSE Special Issue May Edition

Section

Research Article

License

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