New Distance Measure based on the Domain for Categorical Data

被引：0
|
作者：
Aranganayagi, S. [1 ]
Thangavel, K. [2 ]
Sujatha, S. [3 ]
机构：
[1] JKK Nataraja Coll Arts & Sci, Komarapalayam 638183, Tamil Nadu, India
[2] Periyar Univ, Dept Comp Sci, Salem 636011, Tamil Nadu, India
[3] Kongu Engn Coll, Dept Comp Applicat, Perundurai 638052, Tamil Nadu, India
来源：
FIRST INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING 2009 (ICAC 2009) | 2009年
关键词：
D O I：
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中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Clustering the process of grouping homogeneous objects is an important data mining process. Few algorithms exist to cluster categorical data. K-Modes is the scalable and efficient algorithm to cluster the categorical data. In this paper we propose a new distance measure for K-Modes based on the cardinality of domain of attribute. The proposed method is experimented with data sets obtained from UCI data repository. Results prove that the proposed measure generates better clusters than the K-Modes algorithm.
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页码：93 / +
页数：2
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