Comparative Analysis of Roughness with Maximum Dependency Attribute

Authors

  • Rani MJ Department of Computer Science, M.V.Muthiah Govt.Arts College for Women, Dindigul, Tamil Nadu, India
  • Pethalakshmi A Department of Computer Science, M.V.Muthiah Govt.Arts College for Women, Dindigul, Tamil Nadu, India.

Keywords:

Rough Clustering, Equivalence Classes, Roughness, Maximum Dependency Attribute, Purity Analysis

Abstract

Rough set theory is a powerful mathematical tool that has been applied widely to extract knowledge from many databases. It deals with inexact and incomplete data. Cluster analysis means finding similarities between data according to the characteristics found in the data and grouping similar data objects into clusters that are meaningful, useful, or both. Data clustering techniques are valuable tools for researchers working with large databases of multivariate data. Cluster analysis is used in various applications viz., Pattern Recognition, Data Analysis, Image Processing and so on. This paper analyses Roughness and Maximum Dependency Attribute clustering algorithms that minimizes the need for subjective human intervention and compare the purity analysis between these two methods. Purity analysis percentage is calculated from the result of final clusters. Six datasets are used in this research work for comparing the roughness and maximum dependency attribute algorithm to describe the cluster solution by using the purity analysis (PA).

References

Arun K Pujari, “Data Mining Techniques”, Universities Press (India) Private Limited, 2010.

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Published

2025-11-13

How to Cite

[1]
M. Rani and A. Pethalakshmi, “Comparative Analysis of Roughness with Maximum Dependency Attribute”, Int. J. Comp. Sci. Eng., vol. 6, no. 4, pp. 145–149, Nov. 2025.

Issue

Vol. 6 No. 4 (2018): IJCSE Special Issue May Edition

Section

Review Article

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