Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

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Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values (1998)

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by Zhexue Huang

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264 - 3 self

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@MISC{Huang98extensionsto,
    author = {Zhexue Huang},
    title = {Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values},
    year = {1998}
}

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Abstract

The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present two algorithms which extend the k-means algorithm to categorical domains and domains with mixed numeric and categorical values. The k-modes algorithm uses a simple matching dissimilarity measure to deal with categorical objects, replaces the means of clusters with modes, and uses a frequency-based method to update modes in the clustering process to minimise the clustering cost function. With these extensions the k-modes algorithm enables the clustering of categorical data in a fashion similar to k-means. The k-prototypes algorithm, through the definition of a combined dissimilarity measure, further integrates the k-means and k-modes algorithms to allow for clustering objects described by mixed numeric and categorical attributes. We use the well known soybean disease and credit approval data sets to demonstrate the clustering performance of the two algorithms. Our experiments on two real world data sets with half a million objects each show that the two algorithms are efficient when clustering large data sets, which is critical to data mining applications.

Keyphrases

large data set categorical value k-means algorithm k-modes algorithm k-prototypes algorithm credit approval data set numeric value real world data soybean disease categorical object categorical data clustering cost function categorical attribute clustering process simple matching dissimilarity measure combined dissimilarity measure categorical domain real world data set mining application frequency-based method

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