Association Rule Mining Algorithm for Web Search Result Optimization- a Review

Abstract

The web is an enormous information space where large number of an individual article or unit such as documents, images, videos or other multimedia can be retrieved. In this context, several information technologies have been developed to assist users to gratify their searching needs on web, and the most used by users are search engines as Yahoo, Google, Netscape, e-Bay, e -Trade, Expedia, Amazon, Bing, Ask, and so on. The search engines allow users to find web relevant resources by setting up their queries and reviewing a list of answers. In this paper, a search result optimization method for search engine optimization by page rank updating, query recommendation and query reformulation are proposed. It explores the user’s queries registered in the search engine's query logs in order to learn how users search and also in order to design algorithms that can improve the correctness of the answers suggested to users. The proposed method starts by exploring the query logs to find query clusters and identify session of queries, then it examines the query logs to discover useful relationship among pages, keywords and queries within clusters using association rule mining algorithms such as apriori algorithm and automated apriori algorithm. The authors also showed that automated apriori algorithm generates more strong rules as compared to apriori algorithm.