An Efficient Algorithm for Incremental Mining of Association Rules

Authors:
Chin-Chen Chang;Yu-Chiang Li;Jung-San Lee
Affiliations:
Feng Chia University and National Chung Cheng University;National Chung Cheng University;National Chung Cheng University
Venue:
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Year:
2005

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Abstract

Incremental algorithms can manipulate the results of earlier mining to derive the final mining output in various businesses. This study proposes a new algorithm, called the New Fast UPdate algorithm (NFUP) for efficiently incrementally mining association rules from large transaction database. NFUP is a backward method that only requires scanning incremental database. Rather than rescanning the original database for some new generated frequent itemsets in the incremental database, we accumulate the occurrence counts of newly generated frequent itemsets and delete infrequent itemsets obviously. Thus, NFUP need not rescan the original database and to discover newly generated frequent itemsets. NFUP has good scalability in our simulation.