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Association Rule Algorithm Based on Bitmap and Granular Computing HONG-ZHEN ZHENG1, DIAN-HUI CHU, DE-CHEN ZHAN 1,2 College of Computer Science & Technology, Harbin Institute of Technology, Weihai, 264209, China 3College of Computer Science & Technology, Harbin Institute of Technology, Harbin, 150001, ChinaIt presents an association rule algorithm based on granular computing that doesn’t follow the generation-and-test strategy of Apriori algorithm It adopts the divide and conquers strategy, thus avoids the time consuming table scan to find and prune the itemsets. It is the fast bit operations based on its corresponding granular for all the operations of finding large itemsets from the datasets. The experimental result of the algorithm with Apriori, AprioriTid and Apriori Hybrid algorithms shows Bit- Association Rule is 2 to 3 orders of magnitudes faster. Our research indicates that bitmap and granular computing can greatly improve the performance of association rule algorithm, and are very promising for data mining applications. Keywords: Data mining; Association rules; Bitmap.
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BibTex: @ARTICLE{P1120529001, AUTHOR = {HONG-ZHEN ZHENG and DIAN-HUI CHU and DE-CHEN ZHAN}, TITLE = {Association Rule Algorithm Based on Bitmap and Granular Computing},
JOURNAL = {The International Journal of Artificial Intelligence and Machine Learning},
YEAR = {2005}, VOLUME = {5}, ISSUE ={III}, PAGE= {51--54} } ( |
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