Clustering transactions using large items
Proceedings of the eighth international conference on Information and knowledge management
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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We explore in this paper the efficient clustering of itemdata. Different from those of the traditional data, the featuresof item data are known to be of high dimensionalityand sparsity. In view of the features of item data, we devisein this paper a novel measurement, called the association-taxonomysimilarity, and utilize this measurement to performthe clustering. With this association-taxonomy similaritymeasurement, we develop an efficient clustering algorithm,called algorithm AT (standing for Association-Taxonomy),for item data. Two validation indexes basedon association and taxonomy properties are also devised toassess the quality of clustering for item data. As validatedby the real dataset, it is shown by our experimental resultsthat algorithm AT devised in this paper significantly outperformsthe prior works in the clustering quality as measuredby the validation indexes, indicating the usefulness ofassociation-taxonomy similarity in item data clustering.