A Neural Net Approach to Data Mining: Classification of Users to Aid Information Management

Abstract

Techniques from the domain of Artificial Intelligence are used increasingly to combat the problem of information overload on the Internet. The vast majority of such techniques and related systems attempt to overcome the problems of information overload by automating the analysis of the content of online documents. In many web-sites (document repositories and e-commerce sites) system logs, documenting user behaviour, are available and can be used as a valuable resource in information management. This information represents a valuable resource to aid in the organisation of information and the presentation of such information to users. In many such systems this information can be represented using a set of tuples indicating which pages/items were visited by a user. Using this information can provide many advantages. A classification of tuples can be used to aid information management and we outline briefly systems which have used the classification algorithm we propose. This paper presents an approach to solving classification problems by combining feature selection and neural networks. The main idea is to use techniques from the field of information theory to select a set of important attributes that can be used to classify tuples. A neural network is trained using these attributes; the neural network is then used to classify tuples. In this paper, we discuss data mining, review common approaches and outline our algorithm. We also present preliminary results obtained against a well-known data collection.