An alternative extension of the k-means algorithm for clustering categorical data

• Autorzy: Ohn Mar San , Van-Nam Huynh , Yoshiteru Nakamori
• Języki publikacji: EN

Abstrakty

EN

Most of the earlier work on clustering has mainly been focused on numerical data whose inherent geometric properties can be exploited to naturally define distance functions between data points. Recently, the problem of clustering categorical data has started drawing interest. However, the computational cost makes most of the previous algorithms unacceptable for clustering very large databases. The -means algorithm is well known for its efficiency in this respect. At the same time, working only on numerical data prohibits them from being used for clustering categorical data. The main contribution of this paper is to show how to apply the notion of 'cluster centers' on a dataset of categorical objects and how to use this notion for formulating the clustering problem of categorical objects as a partitioning problem. Finally, a -means-like algorithm for clustering categorical data is introduced. The clustering performance of the algorithm is demonstrated with two well-known data sets, namely, em soybean disease and em nursery databases.

Słowa kluczowe

EN

data mining; cluster analysis; categorical data

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2004
• Tom: 14
• Numer: 2
• Strony: 241-247

Daty

wydano

2004

otrzymano

2003-08-02

poprawiono

2004-04-01

Twórcy

autor

Ohn Mar San

• Mathematics and Statistics Department CoOperative Degree College, Sagaing, Myanmar

autor

Van-Nam Huynh

• Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Tatsunokuchi, Ishikawa 923-1292, Japan

autor

Yoshiteru Nakamori

• Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Tatsunokuchi, Ishikawa 923-1292, Japan

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