Clustering Categorical Data Using a Swarm-based Method

被引：0

作者：

Izakian, Hesam ^{[1
]}

Abraham, Ajith ^{[1
]}

Snasel, Vaclav ^{[2
]}

机构：

[1] MIR Labs, Machine Intelligence Res Labs, Auburn, WA 98071 USA

[2] VSB Tech Univ Ostrava, Fac Elect Engn & Comp Sci, Ostrava, Czech Republic

来源：

2009 WORLD CONGRESS ON NATURE & BIOLOGICALLY INSPIRED COMPUTING (NABIC 2009) | 2009年

关键词：

clustering; categorical data; swarm based optimization; ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The K-Modes algorithm is one of the most popular clustering algorithms in dealing with categorical data. But the random selection of starting centers in this algorithm may lead to different clustering results and falling into local optima. In this paper we proposed a swarm-based K-Modes algorithm. The experimental results over two well known Soybean and Congressional voting categorical data sets show that our method can find the optimal global solutions and can make up the K-Modes shortcoming.

引用

页码：1719 / +

页数：2

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