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Genetic algorithm for clustering mixed-type data

[+] Author Affiliations
Shiueng-Bien Yang

Wenzao Ursuline College of Languages, Department of Information Management and Communication, 900 Mintsu 1st Road, Kaohsing 807, Taiwan

Yung-Gi Wu

Chang Jung Cheistian University, Department of Information Science and Engineering, 396 Chang Jung Road, Sec.1, Kway Jen, Tainan 71101, Taiwan

J. Electron. Imaging. 20(1), 013003 (February 08, 2011). doi:10.1117/1.3537836
History: Received April 10, 2010; Revised August 12, 2010; Accepted December 03, 2010; Published February 08, 2011; Online February 08, 2011
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k-modes algorithm was recently proposed to cluster mixed-type data. However, in solving clustering problems, the k-modes algorithm and its variants usually ask the user to provide the number of clusters in the data sets. Unfortunately, the number of clusters is generally unknown to the user. Therefore, clustering becomes a tedious task of trial-and-error and the clustering result is often poor, especially when the number of clusters is large and not easy to guess. Also, it is hard for a user to select the weight between categorical and numeric attributes in the k-modes algorithm. In this paper, a genetic algorithm for clustering large data sets with mixed-type data is proposed, and this algorithm can automatically search the number of clusters in the data set. Also, a weight can be automatically selected by the genetic algorithm to prevent favoring either type of attribute. Experimental results illustrate the effectiveness of the genetic algorithm.

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Citation

Shiueng-Bien Yang and Yung-Gi Wu
"Genetic algorithm for clustering mixed-type data", J. Electron. Imaging. 20(1), 013003 (February 08, 2011). ; http://dx.doi.org/10.1117/1.3537836


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