Incremental sequential pattern mining algorithms of Web site access in grid structure database. Issue 3 (March 2017)

• Record Type: Journal Article
• Authors: Liu, Dawei; Cai, Saifeng; Guo, Xiaohong
• Is Part Of: Neural computing & applications. Volume 28:Issue 3(2017)
• Page Start: 575
• Page End: 583
• Publication Date: 2017-03
• Subjects: Data mining -- Sequential pattern mining -- Web log -- Web access pattern -- Grid structure; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2096-4
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Sequential pattern mining is a process of knowledge discovery which finds the frequent subsequence as a mode from the sequence database. Web log database is typically dynamic. Web log records are generated constantly, and user access patterns will change accordingly. This study focused on taking advantage of the dynamic characteristics of the Web access database, delivering a fast and efficient incremental mining algorithm. An IncWTP algorithm suitable for Web access sequence mode is developed to handle non-simple path with dynamic data storage structure, and detect and delete the failed sequence data timely.