Algorithm of Frequent Item Sets Mining Based on Index Table

Abstract:

The paper gave a new frequent item sets mining algorithm based on index table at multiple times for the Apriori algorithm scans the database which causes the I/O load is too large, and the costly problem with the Apriori algorithm which want to have a big candidate sets. The algorithm first generated a one-dimensional index table by scan the database once, and then generates a two-dimensional index table based on the one-dimensional index table. After the two-dimension index table had been generated, we can use the method similar with Floyd algorithm, which inserts the single index entry individually into the two-dimensional index table. If the count of new index value is greater than or equal to Minsuppor after the single index item had been inserted, the new index entrys Item will be a frequently item sets. After all single index entry had been inserted into the two-dimensional index table, all the index entry in the table will be the maximum frequently item sets. After analysis we can see that this algorithm has low cost and with the high accuracy than Apriori algorithm and can provide some reference for related rules.

• Periodical: Applied Mechanics and Materials (Volumes 373-375)
• Pages: 1076-1079
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.373-375.1076
• Online since: August 2013
• Authors: Lin Zhang, Nan Zhen Yao, Jian Li Zhang
• Keywords: Algorithm (GA), Data Mining (DM), Frequent Item Set, Index

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