NDLI: An efficient algorithm for incrementally mining frequent closed itemsets

Content Provider	Springer Nature Link
Author	Yen, Show Jane Lee, Yue Shi Wang, Chiu Kuang
Copyright Year	2013
Abstract	The purpose of mining frequent itemsets is to identify the items in groups that always appear together and exceed the user-specified threshold of a transaction database. However, numerous frequent itemsets may exist in a transaction database, hindering decision making. Recently, the mining of frequent closed itemsets has become a major research issue because sets of frequent closed itemsets are condensed yet complete representations of frequent itemsets. Therefore, all frequent itemsets can be derived from a group of frequent closed itemsets. Nonetheless, the number of transactions in a transaction database can increase rapidly in a short time period, and a number of the transactions may be outdated. Thus, frequent closed itemsets may be changed with the addition of new transactions or the deletion of old transactions from the transaction database. Updating previously closed itemsets when transactions are added or removed from the transaction database is challenging.This study proposes an efficient algorithm for incrementally mining frequent closed itemsets without scanning the original database. The proposed algorithm updates closed itemsets by performing several operations on the previously closed itemsets and added/deleted transactions without searching the previously closed itemsets. The experimental results show that the proposed algorithm significantly outperforms previous methods, which require a substantial length of time to search previously closed itemsets.
Starting Page	649
Ending Page	668
Page Count	20
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	40
Issue Number	4
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2013-12-22
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data mining Frequent itemsets Closed itemsets Transaction database Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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