Dynamic web document classification in e-crm using neuro-fuzzy approach

Abstract

Internet technology enables companies to capture new customers, track their performances and online behavior, and customize communications, products, services, and price. The analysis of customers and customer interactions for electronic customer relationship management (e-CRM) can be performed by data-mining (DM), optimization methods, or combined approaches. Some of web mining techniques include analysis of user access patterns, web document clustering and classification. Most existing methods of classification are based on a model that assumes a fixed-size collection of keywords or key terms with predefined set of categories. We propose a new approach to obtain category-keyword sets with unknown number of categories. On the basis of the training set of Web documents, the approach is used to classify test documents into a set of initial categories. Finally evolutionary rules are applied to these new sets of keywords and training documents to update the category-keyword sets to realize dynamic document classification.