NDLI: Mining frequent closed inter-sequence patterns efficiently using dynamic bit vectors

Content Provider	Springer Nature Link
Author	Le, Bac Tran, Minh Thai Vo, Bay
Copyright Year	2014
Abstract	Mining frequent sequences is a critical stage before rule generation for sequence databases. Currently, there are two main ways for mining frequent sequences, namely intra-sequence mining and inter-sequence mining. Inter-sequence mining is more attractive than intra-sequence mining because it considers the relationship between sequences in transactions. However, mining all possible frequent inter-sequences takes a long time and requires a lot of memory. Mining frequent closed inter-sequences is efficient because such sequences are compact, and only the necessary information is maintained. CISP-Miner was proposed for mining frequent closed inter-sequence patterns, but it consumes a lot of memory since many closed patterns are mined. This paper proposes an algorithm called ClosedISP for mining frequent closed inter-sequence patterns. The proposed algorithm uses a checking scheme for early eliminating and checking closed patterns without candidate maintenance. ClosedISP uses a dynamic bit vector that combines transaction information to compress data. In addition, ClosedISP adopts a prefix tree and a depth-first search order to reduce the search space and generate non-redundant sequential rules efficiently. Experiments were conducted to compare the proposed algorithm with CISP-Miner to demonstrate the effectiveness of the proposed algorithm in terms of runtime and memory usage.
Starting Page	74
Ending Page	84
Page Count	11
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	43
Issue Number	1
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2015-01-13
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dynamic bit vector Closed inter-sequence pattern Vertical data format Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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