An interactive classification of Web documents by self-organizing maps and search engines

Abstract

We propose an effective classification view mechanism for hypertext data such as Web documents based on Kohonen's self-organizing map (SOM) and search engines. Web documents collected by search engines are automatically classified by SOM and the obtained SOMs are incrementally modified according to the interaction between users and SOMs. At present, various search engines are designed to retrieve Web documents. When we use search engines to retrieve Web documents we get a lot of answers and have to examine each Web document. Therefore, in order to make up for search engines, we need a function to classify Web documents corresponding to the user's point of view and their purposes. Furthermore, we cannot retrieve pertinent Web documents by conventional search engines when a specific topic is described by more than one Web document. To solve these problems, we exploited a content-based clustering system for Web documents. In this system, Web documents are automatically clustered by their feature vectors produced from Web documents or minimal subgraphs consisting of multiple Web documents, and their overview maps are dynamically generated by SOM. Furthermore, we propose a method by which an obtained SOM is modified by user's interaction such as feedback operations.