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A Bottom-Up Projection Based Algorithm for Mining High Utility Itemsets
Erwin, A., Gopalan, R.P. and Achuthan, N.R.
Mining High Utility Itemsets from a transaction database
is to find itemsests that have utility above a user-specified
threshold. This problem is an extension of Frequent
Itemset Mining, which discovers itemsets that occur
frequently (i.e. with occurrence count larger than a user
given value). The problem of finding High Utility Itemsets
is challenging, because the anti-monotone property so
useful for pruning the search space in conventional
Frequent Itemset Mining does not apply to it. In this paper
we propose a new algorithm called CTU-PRO that mines
high utility itemsets by bottom up traversal of a
compressed utility pattern (CUP) tree. We have tested our
algorithm on several sparse and dense data sets,
comparing it with the recent algorithms for High Utility
Itemset Mining and the results show that our algorithm
works more efficiently. |
Cite as: Erwin, A., Gopalan, R.P. and Achuthan, N.R. (2007). A Bottom-Up Projection Based Algorithm for Mining High Utility Itemsets. In Proc. 2nd International Workshop on Integrating Artificial Intelligence and Data Mining (AIDM 2007), Gold Coast, Queensland, Australia. CRPIT, 84. Ong, K.-L., Li, W. and Gao, J., Eds. ACS. 3-10. |
(from crpit.com)
(local if available)
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