Performance Analysis of Various Entropy Measures in Categorical Data Clustering

被引：0

作者：

Sharma, Shachi ^{[1
]}

Pemo, Sonam ^{[1
]}

机构：

[1] South Asian Univ, Dept Comp Sci, New Delhi, India

来源：

2020 INTERNATIONAL CONFERENCE ON COMPUTATIONAL PERFORMANCE EVALUATION (COMPE-2020) | 2020年

关键词：

Categorical data; clustering; performance analysis; Renyi entropy; Shannon entropy; Tsallis entropy;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Categorical data clustering is gaining importance in recent years. Shannon entropy based similarity and dissimilarity measures are popular for clustering categorical data. However, there are several other generalized entropy measures like Renyi and Tsallis. The paper presents a thorough comparative study of performance of various entropy measures in clustering categorical data. Exhaustive experimental analysis reveals that Renyi entropy measure outperforms other measures in clustering categorical data.

引用

页码：592 / 595

页数：4

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