Siddharth Dawar
ABSTRACT
High-utility itemset mining is an emerging research area in the field of Data Mining. Several algorithms were proposed to find high-utility itemsets from transaction databases and use a data structure called UP-tree for their working. However, algorithms based on UP-tree generate a lot of candidates due to limited information availability in UP-tree for computing utility value estimates of itemsets. In this paper, we present a data structure named UP-Hist tree which maintains a histogram of item quantities with each node of the tree. The histogram allows computation of better utility estimates for effective pruning of the search space. Extensive experiments on real as well as synthetic datasets show that our algorithm based on UP-Hist tree outperforms the state of the art pattern-growth based algorithms in terms of the total number of candidate high utility itemsets generated that needs to be verified.
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Index Terms
- UP-Hist Tree: An Efficient Data Structure for Mining High Utility Patterns from Transaction Databases
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