Webpage Personalization based Page Ranking System Using Weighted Fuzzy C-means Clustering and Whale Optimization

P. Pranitha, M.A.H. Farquad and Dr.G. Narshimha

To improve the enhancement in results of page ranking, the web page personalization system plays a vital role in websites. In Traditional clustering and optimization algorithms, such as KNN and Fuzzy, Neural Network, current hitches in giving proper outcomes to web page personalization researches. Considerable improvised results were obtained in this proposed page ranking phase. Hence obtained the better clustering and optimization outcomes, and this was implemented the step-by-step approach Whale optimization technique besides weighted FCM method. Here key intention of the investigation is towards personalizing web search by using the f optimal ranked URLs that overcomes the disadvantages of our existing research links analysis and personalized web search ranking. The proposed methodology encompasses 2 phases offline and online. In the input query to be given in to the query expansion phase which is not salvaged. The main objective of the expanding the query is to accept for salvage documents which do not consist the standings of the query. In the pre-processing stage to collect a visited webpages and that webpages are converted into numerical matrix then; cluster that numerical matrix with the aid of WFCM. In the WFCM process to generate a vector for the user query, and then find a distance between users a query vector, after that to select a minimum distance centroid. Finally, the WOA is used to optimize the best top n number of pages. The application will be done in JAVA and the function of the proposed technique will be analysed with numerous available systems.

Volume 11 | Issue 6

Pages: 83-94