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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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Clustering algorithms for categorical data
Clustering algorithms for categorical data
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International Journal of Data Mining and Bioinformatics
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AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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A challenge involved in applying density-based clustering to categorical datasets is that the 'cube' of attribute values has no ordering defined. We propose the HIERDENC algorithm for hierarchical density-based clustering of categorical data. HIERDENC offers a basis for designing simpler clustering algorithms that balance the tradeoff of accuracy and speed. The characteristics of HIERDENC include: (i) it builds a hierarchy representing the underlying cluster structure of the categorical dataset, (ii) it minimizes the user-specified input parameters, (iii) it is insensitive to the order of object input, (iv) it can handle outliers. We evaluate HIERDENC on small-dimensional standard categorical datasets, on which it produces more accurate results than other algorithms. We present a faster simplification of HIERDENC called the MULIC algorithm. MULIC performs better than subspace clustering algorithms in terms of finding the multi-layered structure of special datasets.