A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining

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A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining (1997)

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by Zhexue Huang

Venue:	In Research Issues on Data Mining and Knowledge Discovery
Citations:	120 - 2 self

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@INPROCEEDINGS{Huang97afast,
    author = {Zhexue Huang},
    title = {A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining},
    booktitle = {In Research Issues on Data Mining and Knowledge Discovery},
    year = {1997},
    pages = {1--8}
}

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Abstract

Partitioning a large set of objects into homogeneous clusters is a fundamental operation in data mining. The k-means algorithm is best suited for implementing this operation because of its efficiency in clustering large data sets. However, working only on numeric values limits its use in data mining because data sets in data mining often contain categorical values. In this paper we present an algorithm, called k-modes, to extend the k-means paradigm to categorical domains. We introduce new dissimilarity measures to deal with categorical objects, replace means of clusters with modes, and use a frequency based method to update modes in the clustering process to minimise the clustering cost function. Tested with the well known soybean disease data set the algorithm has demonstrated a very good classification performance. Experiments on a very large health insurance data set consisting of half a million records and 34 categorical attributes show that the algorithm is scalable in terms of ...

Keyphrases

data mining fast clustering algorithm cluster large categorical data set good classification performance large data set data set fundamental operation numeric value homogeneous cluster categorical object large health insurance data new dissimilarity measure clustering cost function large set categorical attribute contain categorical value k-means algorithm categorical domain clustering process k-means paradigm soybean disease data

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