Progressive subspace skyline clusters mining on high dimensional data

  • Authors:

  • Rong Hu;Yansheng Lu;Lei Zou;Chong Zhou

  • Affiliations:

  • School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

  • Venue:
  • PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
  • Year:
  • 2007

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Abstract

Skyline queries have caused much attention for it helps users make intelligent decisions over complex data. Unfortunately, too many or too few skyline objects are not desirable for users to choose. Practically, users may be interested in the skylines in the subspaces of numerous candidate attributes. In this paper, we address the important problem of recommending skyline objects as well as their neighbors in the arbitrary subspaces of high dimensional space. We define a new concept, subspace skyline cluster, which is a compact and meaningful structure to combine the advantages of skyline computation and data mining. Two algorithms Sorted-based Subspace Skyline Clusters Mining, and Threshold-based Subspace Skyline Clusters Mining are developed to progressively identify the skyline clusters. Our experiments show that our proposed approaches are both efficient and effective.