HYBRID TECHNIQUES (HMM RANKING AND FUZZY C-MEANS) USED IN WEB MINING FOR GENERATING EFFICIENT RULES
Tripti Saxena
Asst Prof , Computer Science & Engineering, Bhopal, India , IN
Dr.Pratima Gautam
Asst Prof , Computer Science & Engineering, Bhopal, India , IN
Keywords: Web Mining, Clustering, Data prepprocessing, WebPage Ranking, URLs and Products
Abstract
During the previous couple of years the World Wide Web has turned into the greatest and most famous method for communiqué & info distribution. It serve as a stage for exchange different sorts of info. The amount of info accessible on the web is expanding quickly with the hazardous development of the World Wide Web & the approach of e-Commerce. While users are provided with more information and service options, it has become more difficult for them to find the “right” or “interesting” information, the
issue regularly known as info overload. It is notable that more than 80% of the time necessary for complete
any real world data mining project is generally spent on information pre-processing. Information preprocessing lays the preparation for data mining. Web mining is to find out & concentrate valuable data from the World Wide Web. It includes the programmed disclosure of patterns from at-least one Web servers. In this paper, HMM Ranking and FCM clustering used for the generation of better rules to improve the purchase products from the particular website.
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