Paper Info
Title
Incrementally mining high utility patterns based on pre-large concept
Abstract
In traditional association rule mining, most algorithms are designed to discover frequent itemsets from a binary database. Utility mining was thus proposed to measure the utility values of purchased items for revealing high utility itemsets from a quantitative database. In the past, a two-phase high utility mining algorithm was thus proposed for efficiently discovering high utility itemsets from a quantitative database. In dynamic data mining, transactions may be inserted, deleted, or modified from a database. In this case, a batch mining procedure must rescan the whole updated database to maintain the up-to-date information. Designing an efficient approach for handling dynamic databases is thus a critical research issue in utility mining. In this paper, an incremental mining algorithm is proposed for efficiently maintaining discovered high utility itemsets based on pre-large concepts. Itemsets are first partitioned into three parts according to whether they have large (high), pre-large, or small transaction-weighted utilization in the original database and in inserted transactions. Individual procedures are then executed for each part. Experimental results show that the proposed incremental high utility mining algorithm outperforms existing algorithms. © 2013 Springer Science+Business Media New York.
Year
DOI
Venue
2014
10.1007/s10489-013-0467-z
Applied Intelligence
Keywords
Field
DocType
Utility mining,Pre-large itemset,High utility itemset,Incremental mining,Two-phase approach
Data mining,Utility mining,GSP Algorithm,Computer science,Association rule learning,Dynamic data,Two phase approach,Artificial intelligence,Data mining algorithm,Machine learning,Binary number
Journal
Volume
Issue
ISSN
40
2
0924-669X
Citations 
PageRank 
References 
18
0.63
28
Authors
5
Name
Order
Citations
PageRank
Chun-Wei Lin11484154.11
Tzung-pei Hong23768483.06
Guo-Cheng Lan333219.45
Wong Jia-Wei4361.93
Wen-Yang Lin539935.72