Combination of dynamic bit vectors and transaction information for mining frequent closed sequences efficiently. (February 2015)

• Record Type: Journal Article
• Authors: Tran, Minh-Thai; Le, Bac; Vo, Bay
• Is Part Of: Engineering applications of artificial intelligence. Volume 38(2015:Feb.)
• Page Start: 183
• Page End: 189
• Publication Date: 2015-02
• Subjects: Dynamic bit vector -- Frequent closed sequence -- CloFS-DBV; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2014.10.021
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Sequence mining algorithms attempt to mine all possible frequent sequences. These algorithms produce redundant results, increasing the required storage space and runtime, especially for large sequence databases. In recent years, many studies have proved that mining frequent closed sequences is more efficient than mining all frequent sequences. The desired information can be fully extracted from frequent closed sequences. Most algorithms for mining frequent closed sequences use a candidate maintenance-and-test paradigm. The present paper proposes an algorithm called CloFS-DBV that uses dynamic bit vectors. Various methods are employed to reduce memory usage and runtime. Experimental results show that CloFS-DBV is more efficient than the BIDE and CloSpan algorithms in terms of execution time and memory usage.