A flexible and efficient sequential pattern mining algorithm. (13th August 2009)

• Record Type: Journal Article
• Authors: Lin, Jie-Ru; Hsieh, Chia-Ying; Yang, Don-Lin; Wu, Jungpin; , Ming-Chuan Hung
• Is Part Of: International journal of intelligent information and database systems. Volume 3:Number 3(2009)
• Page Start: 291
• Page End: 310
• Publication Date: 2009-08-13
• Subjects: data mining -- sequential pattern mining -- enumeration -- minimum support -- sequential patterns -- sequence databases; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

Sequential pattern mining has gathered great attention in recent years due to its broad applications. Most of the existing methods are in two categories: 1) candidate-generation-and-test approaches such as GSP, requiring multiple database scans, 2) pattern-growth approaches such as PrefixSpan, scanning the projected database which may be several times larger than the original database. Methods from both categories must set minimum support thresholds in advance. To remedy the problems, we propose a new approach, Fast Sequential Pattern Enumeration (FSPE), to mine sequential patterns without the need to predetermine the minimum support threshold. The FSPE scans the transaction database only once to enumerate all candidate sequences with efficient indexing of their support counters. Using our approach one can easily produce meaningful rules for any item that appears at least once in the sequence database.