An Efficient Fuzzy Clustering Algorithm for Mining User Session Clusters on Web Log Data - Mallik, Moksud Alam; Zulkurnain, Nurul Fariza (International Journal of Informatics, Information System and Computer Engineering (INJIISCOM), 2021)
Data mining is extremely vital to get important information from the web. Additionally, web usage mining (WUM) is essential for companies. WUM permits organizations to create rich information related to the eventual fate of their commercial capacity. The utilization of data that is assembled by Web Usage Mining gives the organizations the capacity to deliver results more compelling to their organizations and expanding of sales. Client access patterns can be mined from web access log information using Web Usage Mining (WUM) techniques. Because there are so many end-user sessions and URL resources, the size of web user session data is enormous. Human communications and non-deterministic browsing patterns increment equivocalness and dubiousness of client session information. The fuzzy set-based approach can solve most of the challenges listed above. This paper proposes an efficient Fuzzy Clustering algorithm for mining client session clusters from web access log information to find the groups of client profiles. In addition, the methodologies to preprocess the net log data as well as data cleanup client identification and session identification are going to be mentioned. This incorporates the strategy to do include choice (or dimensionality decrease) and meeting weight task assignments