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Cartesian contour: a concise representation for a collection of frequent sets
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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
Paris, France
SESSION: Research track papers table of contents
Pages 417-426  
Year of Publication: 2009
ISBN:978-1-60558-495-9
Authors
Ruoming Jin  Kent State University, Kent, OH, USA
Yang Xiang  Kent State University, Kent, OH, USA
Lin Liu  Kent State University, Kent, OH, USA
Sponsors
ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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ABSTRACT

In this paper, we consider a novel scheme referred to as Cartesian contour to concisely represent the collection of frequent itemsets. Different from the existing works, this scheme provides a complete view of these itemsets by covering the entire collection of them. More interestingly, it takes a first step in deriving a generative view of the frequent pattern formulation, i.e., how a small number of patterns interact with each other and produce the complexity of frequent itemsets. We perform a theoretical investigation of the concise representation problem and link it to the biclique set cover problem and prove its NP-hardness. We develop a novel approach utilizing the technique developed in frequent itemset mining, set cover, and max k-cover to approximate the minimal biclique set cover problem. In addition, we consider several heuristic techniques to speedup the construction of Cartesian contour. The detailed experimental study demonstrates the effectiveness and efficiency of our approach.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Ruoming Jin: colleagues
Yang Xiang: colleagues
Lin Liu: colleagues