WS-BD-Based Two-Level Match: Interesting Sequential Patterns and Bayesian Fuzzy Clustering for Predicting the Web Pages from Weblogs

Abstract

Abstract The rapid increase in information and technology has led to the increased amount of web pages, which raises the complexity in sticking to relevant web pages, and the visitor suffers due to wastage of time resulting in lack of satisfaction. This paper proposes a web page prediction method using a weighed support and Bhattacharya distance-based (WS-BD) two-level match. The major aim of the proposed method is to attain customer satisfaction. Initially, interesting sequential patterns are obtained using the weighed support that filters the sequential patterns obtained using a PrefixSpan algorithm based on the frequency, duration and recurrence of the web pages. Interesting sequential patterns are clustered using the proposed dice similarity-based Bayesian fuzzy clustering, and the web page is predicted using the two-level match based on Bhattacharya distance. The experimentation is performed using the CTI and MSNBC data which proves the effectiveness of the proposed method. The proposed method shows 9.59, 21.22 and 10.17% improvement than the existing FCM-KNN in terms of precision, recall and F measure for the CTI dataset. Also, the proposed method shows 2.58, 22.17 and 7.83% improvement than the existing FCM-KNN in terms of precision, recall and F measure for the MSNBC dataset.