he aim of this research is dual: first, to present an overview of recently proposed make interaction with the Web. In particular, the lessons learned on strong points and remaining weaknesses of various Web recommendation techniques are summarized and discussed. In this way, Web usage mining is the form of Web mining for finding the usage patterns from Web data. Moreover, these services to clearly recognize the u needed services by several Web based applications. Along with that, examining data in Web usage mining can help the development and making of Web sites together with their management, personalization, business support services, Web pages recommendation, and system improvement. In other words, the purpose of this research is to make Web page recommendations by using analyzed and preprocessed Web log data. In this way, the concept of clustering and data mining are applied to recognize the patterns. This recommendation system presents Web page recommendations to the users by examining their navigational patterns and It also provides appropriate recommendations to cater to present requirements of users. Along with, the investigational outcomes show an important development in the recommendation efficiency of the proposed system.
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