• P.Pranitha
• M.A.H Farquad
• Dr.G.Narsimha

Web usage mining is the method of mining valuable usage patterns as of the web data. Web personalization uses web usage mining method for the progression of knowledge attainment done by scrutinizing the user directional patterns awareness. Nowadays, the Web is an imperative source of information retrieval, and the users accessing the Web are from different families. The usage information about users is verified in web logs. Studying web log files to extract useful patterns is named Web Usage Mining. Web usage mining approaches consist of clustering, association rule mining, sequential pattern mining etc. The web usage mining approaches can be pragmatic to forecast next page access. As the size of cluster increases due to the rise in web users, it will become predictable need to augment the clusters. This paper proposes a cluster optimization methodology based on fuzzy logic and is used to reduce the redundancy for clustering Fuzzy C-Means (FCM) algorithm is used. Fuzzy cluster hunting algorithm for cluster optimization is used to personalize web page clusters of end users. Clustering is a data mining technique of grouping set of data objects into multiple groups or clusters so that objects inside the cluster have high similarity, but are very disparate to the objects inthe other clusters. Fuzzy C-Means is the most commonly used method where an element may have partial membership ratings in more than one fuzzy cluster. This investigation work makes use of MATLAB language to yield a fuzzy clustering algorithm for a URL database into several numbers of clusters. The clusters as well as the membership function has been implemented using MATLAB. The results obtained from the database detect n-clusters to handle the inaccurate and abstruse result. Future research work deliver a relative analysis of K-Means, Fuzzy C-Means and Improved Fuzzy C-Means clustering techniques that provide appropriate and accurate data analysis in the field of web log mining. This document provides a fair comparison between the four most common symmetric key cryptography algorithms: DES, AES, Blowfish and Twofish. Since the main concern here is the performance of the algorithms in different configurations, the presented comparison takes into account the behavior and performance of the algorithm when different data loads are used. The comparison is made based on these parameters: speed, block size and key size. A simulation program is implemented using C programming and Java programming.

WebLog Mining, K-Means algorithm, Fuzzy C-Meansalgorithm,ImprovedFuzzyC-Meansalgorithm

"Evolution Of Web Log Mining Projected On Improved Fuzzy C-Means Clustering Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.628-638, April-2019, Available :http://www.jetir.org/papers/JETIRAU06097.pdf

"Evolution Of Web Log Mining Projected On Improved Fuzzy C-Means Clustering Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp628-638, April-2019, Available at : http://www.jetir.org/papers/JETIRAU06097.pdf