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Mining of Compact and Lossless High Utility Itemset Using Systolic Tree

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Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The Systolic tree structure improving the processing speed in proposed system. The systolic tree mechanism is used in the transaction database for extracting the frequent pattern itemsets. Systolic tree based rule mining scheme is combined with weighted association rule mining(WARM) process which is used to fetch the frequently accessed itemsets with its weight value. Based on the item request count and span time values, it estimates the weight value. The performance of the proposed systolic tree algorithm for high utility itemset mined results is compared with the earlier methods such as UP-Growth and FP-Growth methods in terms of the parameters like time, memory space, and runtime for each and every number of transaction and educational dataset.
Keywords:Association Rule Mining, Data Mining, Systolic tree mechanism, Utility-based mining


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