Approach for Developing Business Statistics Using Data Web Usage Mining
Gollapudi Vrj Sai Prasad, Malapati Sri Rama Lakshmi Reddy, Kuntam Babu Rao, Chodagam Suresh Kumar · International Journal Of Recent Advances in Engineering & Technology · 2020
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), a field at the intersection of computer science and statistics is the process that attempts to discover patterns in large data sets.It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems process of knowledge discovery in databases also known as KDD.Web mining -is the application of data mining techniques to discover patterns from the Web.According to analysis targets, web mining can be divided into three different types, which are Web usage mining, Web content mining and Web structure mining.Predicting of user's browsing behavior is an important technology of E-commerce application.The prediction results can be used for personalization, building proper web site, improving marketing strategy, promotion, customer likes, product supply, getting marketing information, forecasting market trends, changes according to interests, and increasing the competitive strength of enterprises etc. Web Usage Mining is the application of data mining techniques to discover interesting usage patterns from Web data, in order to understand and better serve the needs of Webbased applications.Web usage mining is usually an automated process whereby Web servers collect and report user access patterns in server access logs.The navigation datasets which are sequential in nature.Clustering web data is finding the groups which share common interests and behavior by analyzing the data collected in the web servers, this improves clustering on web data efficiently using proposed robust algorithm.In the proposed work a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multilayered neural network using the K-means clustering algorithm.The proposed model use multi-layered network architecture with a back propagation learning mechanism to discover and analyze useful knowledge from the available Web log data.