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Closed Constrained Gradient Mining in Retail Databases.
- Source :
- IEEE Transactions on Knowledge & Data Engineering; Jun2006, Vol. 18 Issue 6, p764-769, 6p, 3 Black and White Photographs, 2 Charts, 5 Graphs
- Publication Year :
- 2006
-
Abstract
- Incorporating constraints into frequent itemset mining not only improves data mining efficiency, but also leads to concise and meaningful results. In this paper, a framework for closed constrained gradient itemset mining in retail databases is proposed by introducing the concept of gradient constraint into closed itemset mining. A tailored version of CLOSET+, LCLOSET, is first briefly introduced, which is designed for efficient closed itemset mining from sparse databases. Then, a newly proposed weaker but antimonotone measure, top-X average measure, is proposed and can be adopted to prune search space effectively. Experiments show that a combination of LCLOSET and the top-X average pruning provides an efficient approach to mining frequent closed gradient itemsets. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 18
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 20956664
- Full Text :
- https://doi.org/10.1109/TKDE.2006.88