An immune-based model for Web data mining

Abstract

This paper presents an immune-based model for Web data mining, in which the rule library and statistic dictionary are used to build search schemas, and data from different sources are wrapped to XML. By using Web content mining and Web structure mining, data are abstracted to mediated schemas and then compared with search schemas. As the information about Web page content and its links are optimized, the traditional search strategy based on keyword matching can be improved. At the same time, many ideas of immune system are introduced to increase the search efficiency. Now many Web data mining system using immune approaches by Andrew Seeker, Alex A. Freitas, and Jon Timmis, (2003), H.A. Abbass, R.A. Sarker, and C.S. Newton (2001) are concerned with data clustering, while immunity is mainly used for data processing after clustering in this model.