Web mining has been explored to a vast degree and diverse systems have been proposed for a assortment of applications that join Web Search, Web Classification, Web Personalization, Versatile Web Locales etc. Mining Web structure Data has resulted in assortment of hyper join based algorithms to rank results of a query. Similarly, Web use Data has been utilized to distinguish user-sessions and group them for better forecast of client route patterns. Most relook on Web mining has so far been from a “data-centric” point of view. In this venture we examine the temporal measurement of the Web use data. We study in specific the conduct of Web use Data over a period of time and group pages that follow comparable access patterns. Such kind of examination could be valuable for target marketing based on time or for web services optimization. In the second part of the project, we characterize a new measure called “Page Popularity” that counts the number of hits to Web pages amid a certain time period and giving more weight to the pages that have been accessed frequently amid a “recent” period of time. This kind of examination helps in distinguishing emerging “popular” themes and brings down the bias on any subject that is “obsolete” yet has been accessed a part amid an prior period of time.

Web mining, Web Content, Web Structure, Web Services, Web Clustering

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