Title :
High-Efficiency Algorithm for Mining Maximal Frequent Item Sets Based on Matrix
Author :
Quan, Jiang ; Liu, Zhijing ; Chen, Donghui ; Zhao, Hongwei
Author_Institution :
Sch. of Comput. Sci. & Technol., Xidian Univ., Xi´´an, China
Abstract :
Association Rule Mining is an important data mining technique and Maximal frequent item sets mining is an essential step in the process of Association rule. Here presented is BM-MFI, a new algorithm based on matrix, for mining maximal frequent item sets. Its basic idea is transforming the event database into matrix database by operating the rows and columns of matrix to compress the database. Using Itemset-Tidset pair can mine maximal frequent item sets in the compressed database with convenience and effectiveness, and therefore prevent conditional FP-tree and candidate patterns. Experimental result verifies the efficiency of the BM-MFI.
Keywords :
data mining; sensor fusion; BM-MFI; Itemset-Tidset pair; association rule mining; compressed database; conditional FP-tree; data mining; event database; high-efficiency algorithm; matrix database; maximal frequent item set mining; maximal frequent item sets mining; Algorithm design and analysis; Association rules; Itemsets; Software algorithms; BM-MFI; Itemset-Tidset pair; data mining; frequent item sets; maximal frequent item set;
Conference_Titel :
Computational Intelligence and Communication Networks (CICN), 2012 Fourth International Conference on
Conference_Location :
Mathura
Print_ISBN :
978-1-4673-2981-1
DOI :
10.1109/CICN.2012.123
