ENHANCING PROXY BASED WEB CACHING SYSTEM USING CLUSTERING BASED PRE-FETCHING WITH MACHINE LEARNING TECHNIQUE

Data Mining is a process of extracting knowledge from various data source. Web usage mining is the area of data mining which deals with the discovery and analysis of usage patterns from web data, specifically web logs, in order to improve web based applications. Considering these, Web caching is a Data mining technique which is used to reduce user perceived latency when user is accessing the web pages. Web pre-fetching is the scheme where web pages are pre fetched into the intermediate server (proxy) cache before user accessing it. These two techniques can complement each other since web caching exploits the temporal locality whereas web pre-fetching utilizes the spatial Locality of web objects. To improve the performance of web access, web caching and pre-fetching techniques are being integrated using clustering based pre-fetching algorithm. In this paper, pre-fetching using clustering technique is combined with SVM (Support Vector Machine)-LFU algorithm, a machine learning technique for web proxy caching. By analysis it is shown that SVM technique is better than clustering based pre-fetching technique using caching policy like LFU considering bandwidth utilization and access latency