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Mining Constrained Cube Gradient for the Condensed Data Cube
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    Abstract:

    Constrained cube gradient mining is an important mining task and its goal is to extract the pairs of gradient-probe cell satisfy the gradient constraint from a data cube. However, previous work are explored for a general data cube. In this paper, the problem of the mining constrained cube gradient for a condensed cube is studied. An algorithm named as eLiveSet for the problem is developed through the extension of the existing efficient mining algorithm LiveSet-driven. The experimental results show that the algorithm is more effective than the existing algorithm on the performance of mining constrained cube gradient.

    Reference
    [1]Han J. Towards on-line analytical mining in large databases. SIGMOD Record, 1998,27(1):97～107.
    [2]Palpanas T. Knowledge discovery in data warehouses. SIGMOD Record, 2000,29(3):88～100.
    [3]Abdulghani AA. Cubegrades-Generalization of association rules to mine large datasets [Ph.D. Thesis]. New Brunswick: The State University of New Jersey, 2001.
    [4]Agrawal R, Tomasz I, Arun NS. Mining association rules between sets of items in large databases. In: Buneman P, ed. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1993. 207～216.
    [5]Gray J, Bosworth A, Layman A, Pirahesh H. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-total. In: Stanley YW, ed. Proceedings of the 12th International Conference on Data Engineering. Washington: IEEE Computer Society Press, 1996. 152～159.
    [6]Dong G, Han J, Lam J, Pei J, Wang K. Mining multi-dimensional constrained gradients in data cubes. In: Peter MG, ed. Proceedings of the 27th International Conference on Very Large Data Bases. San Fransisco: Morgan Kaufmann Publishers, 2001. 321～330.
    [7]Wang W, Feng J, Lu H, Jeffrey XY. Condensed cube: An effective approach to reducing data cube size. In: Rakesh A, ed. Proceedings of the 18th International Conference on Data Engineering. Washington: IEEE Computer Society Press, 2002. 155～165.
    [8]Sarawagi S, Agrawal R, Megiddo N. Discovery-Driven exploration of OLAP data cubes. In: Hans-J?rg Schek, ed. Proceedings of the 6th International Conference on Extending Database Technology. New York: Springer-Verlag, 1998. 168～182.
    [9]Beyer K, Ramakrishnan R. Bottom-Up computation of sparse and iceberg CUBE. In: Davidson DB, Delis A, eds. Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1999. 359～370.
    [10]Han J, Pei J, Dong G., Wang K. Efficient computation of iceberg cubes with complex measures. In: Sellis T, ed. Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2001. 1～12.
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冯玉才,刘玉葆,冯剑琳.浓缩数据立方中约束立方梯度的挖掘.软件学报,2003,14(10):1706-1716

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History
  • Received:June 18,2002
  • Revised:June 18,2002
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