Probability Density Based Fuzzy C Means Clustering for Web Usage Mining
Jayanti Mehra1, RS Thakur2
1Jayanti Mehra, PhD. Department of Computer Applications, Maulana Azad National Institute of Technology Bhopal (M.P), India.
2Dr. Ramjeevan Singh Thakur, Associate Professor, Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal (M.P), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 169-173 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2676028419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The World Wide Web is huge repository and it is growing exponentially. It contains vast amount of information which is growing and updating rapidly. Various organizations, institutes, government agencies and service centers update their information regularly. The World Wide Web provides its services to the varieties of web users. Web users may have different interests, needs and backgrounds. Clustering is one of the most important tasks in the active areas of Web Usage Knowledge Discovery. It assures to handle the difficulty of information overload on the Internet while many users are connected on the social media. Clustering is utilized for grouping information into comparative access design for discovering client interest. There are two drawbacks of FCM algorithm, firstly the requirements of no. of clusters c and secondly assigning the primary relationship matrix. Due to these two drawbacks the FCM algorithm is hard to decide about the suitable no. of cluster and this algorithm is insecure. The determination of desirable preliminary cluster is an important problem, therefore a new technique called PDFCM algorithm is described.
Keyword: Clustering, FCM, Probability Based Fuzzy C Means Clustering (PDFCM), Web Log Mining.
Scope of the Article: Clustering