Paper Info
Title
An Efficient Algorithm for Maintaining Frequent Closed Itemsets over Data Stream
Abstract
Data mining refers to the process of revealing unknown and potentially useful information from a large database. Frequent itemsets mining is one of the foundational problems in data mining, which is to discover the set of products that purchased frequently together by customers from a transaction database. However, there may be a large number of patterns generated from database, and many of them are redundant. Frequent closed itemset is a well-known condensed representation of frequent itemset, and it provides complete information of frequent itemsets. Extensive studies have been proposed for mining frequent closed itemsets from transaction database, but most of them do not take streaming data into consideration. In this paper, we propose an efficient algorithm for maintaining frequent closed itemsets over data streams. Whenever a transaction is added to database, our approach incrementally updates the information of closed itemsets and outputs updated frequent closed itemsets based on user-specified thresholds. The experimental results show that our approach outperforms previous studies.
Year
DOI
Venue
2009
10.1007/978-3-642-02568-6_78
IEA/AIE
Keywords
Field
DocType
efficient algorithm,data stream,large database,frequent itemsets,frequent itemset,data mining,transaction database,frequent closed itemset,closed itemsets,frequent itemsets mining,frequent closed itemsets
Data mining,Data stream mining,Data stream,Computer science,Algorithm,Streaming data,Database transaction,Complete information
Conference
Volume
ISSN
Citations 
5579
0302-9743
9
PageRank 
References 
Authors
0.58
9
4
Name
Order
Citations
PageRank
Show-Jane Yen1537130.05
Yue-Shi Lee254341.14
Cheng-Wei Wu332910.89
Chin-Lin Lin4111.03