Title: Optimal page ranking system for web page personalisation using kernel-based fuzzy C-means and gravitational search algorithm
Authors: P. Pranitha; M.A.H. Farquad; G. Narshimha
Addresses: University of JNTUH, Hyderabad, Telangana, India ' Faculty of Computers and Information Systems, Islamic University of Madinah, Saudi Arabia ' JNTUH, Karimnagar, Telangana, India
Abstract: In this personalised web search (PWS), we utilise a kernel-based FCM for clustering a web pages. For effective personalised web search, queries are optimised using GSA with respect to clustered query sessions. In offline processing, initially preprocess the input information taken from consumer visited web pages and are transformed in to numerical matrix. These matrices are gathered with the help of kernel-based FCM method after produce a vector for consumer query and detect a minimum distance as centroid values these values are input to the GSA algorithm. It will engender these links given top N web pages from cluster. In online processing, the user query is engaged as input then extract some web pages from Google, Bing, Yahoo also extract content and snippet from web pages. Finally, detect a sum of contents and snippets and web pages would be considered in descending order.
Keywords: kernel-based fuzzy c-means; clustering; offline; online; preprocessing; Google; Bing; Yahoo.
International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.1, pp.1 - 19
Received: 11 Apr 2017
Accepted: 19 Jun 2017
Published online: 12 Nov 2019 *