Discovering and Mining Sequential Analysis of User Web-Page Traversal Patterns Using Sequential Pattern Derivable Generalized Suffix Tree Discovery Method (Sempat–Dgst)

Abstract

The rising fame of electronic commerce makes data mining a vital technology for a few applications, particularly online business seriousness. Pattern discovery is a significant subfield of web mining that endeavors to find fascinating (or superior grade) patterns from web log files. There are a few effective strategies to find such patterns regarding distinctive interestingness measures. Frequent pattern mining is an intensely explored region in the field of data mining with wide scope of applications. One of them is to utilize frequent pattern discovery methods in Web log data. In this paper we proposed sequential analysis of discovering and mining user web page traversal patterns using sequential pattern derivable generalized suffix tree discovery method (SemPat–DGST). The outcomes shows that proposed method can accomplish 10-20% preferable accuracy over the exclusively use based model, and 5-8% better than an ontology based model.