Clustering categorical data using a swarm-based method

Author

Izakian, Hesam ; Abraham, Ajith ; Sná, Václav

Author_Institution

Machine Intell. Res. Labs. (MIR Labs.), Auburn, WA, USA

fYear

2009

fDate

9-11 Dec. 2009

Firstpage

1720

Lastpage

1724

Abstract

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.

Keywords

category theory; optimisation; pattern clustering; categorical data; categorical data clustering; congressional voting categorical data sets; k modes shortcoming; k-modes algorithm; local optima; optimal global solutions; random selection; soybean voting categorical data sets; swarm based method; Ant colony optimization; Clustering algorithms; Computer science; Cost function; Frequency measurement; Machine intelligence; Particle swarm optimization; Partitioning algorithms; Simulated annealing; Voting; categorical data; clustering; swarm based optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on

Conference_Location

Coimbatore

Print_ISBN

978-1-4244-5053-4

Type

conf

DOI

10.1109/NABIC.2009.5393623

Filename

5393623