Clustering Item Data Sets with Association-Taxonomy Similarity

We explore in this paper the efficient clustering of item data. Different from those of the traditional data, the features of item data are known to be of high dimensionality and sparsity. In view of the features of item data, we devise in this paper a novel measurement, called the associationtaxonomy similarity, and utilize this measurement to perform the clustering. With this association-taxonomy similarity measurement, we develop an efficient clustering algorithm, called algorithm AT (standing for AssociationTaxonomy), for item data. Two validation indexes based on association and taxonomy properties are also devised to assess the quality of clustering for item data. As validated by both real and synthetic datasets, it is shown by our experimental results that algorithm AT devised in this paper significantly outperforms the prior works in the clustering quality as measured by the validation indexes, indicating the usefulness of association-taxonomy similarity in item data cluste...

• Type: Conference
• Year: 2003
• Where: ICDM
• Authors: Ching-Huang Yun, Kun-Ta Chuang, Ming-Syan Chen
• Keywords: Association-taxonomy Similarity, Data Mining, Efficient Clustering, ICDM 2003, Item Data