Extensible Attribute Similarity Data Mining for Categorical Data Streams in Web Usage Framework

Abstract

Information mining is a forcefully idea in data recovery in view of various characteristics from various information sources. Emergence increase of web 2.0 oriented applications for best user encounters and availability without time and geographical limitations. For the researchers, Web usage logs are becoming a major role across the world, and user’s behavior and analysis of data are one of the different concepts for decision making in business intelligence. To employ customer-centric Web-oriented applications, we implement extensible Web usage mining framework (XWUMF), i.e., hybrid framework to handle data extraction based on user behavior with different attributes in data sets. Proposed hybrid approach is the combination of extensible and classification by pattern-based hierarchical clustering (ECPBHC). Extensible and classification by pattern-based hierarchical clustering (ECPBHC) is used to extract relational data from different Web-oriented categorical data sets based on user’s behavior and analysis with different attributes’ relations.