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Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely <i>preprocessing, pattern discovery</i>, and <i>pattern analysis</i>. This paper describes each of these phases in detail. Given(More)
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of <i>recommender systems</i>---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems(More)
Computational techniques that build models to correctly assign chemical compounds to various classes of interest have many applications in pharmaceutical research and are used extensively at various phases during the drug development process. These techniques are used to solve a number of classification problems such as predicting whether or not a chemical(More)
In this paper we study the problem of classifying chemical compound datasets. We present an algorithm that first mines the chemical compound dataset to discover discriminating sub-structures; these discriminating sub-structures are used as features to build a powerful classifier. The advantage of our classification technique is that it requires very little(More)
As structural and functional genomics efforts provide the biological community with ever-broadening sets of interrelated data, the need to explore such complex information for subtle relationships expands. We present wCLUTO, a Web-enabled version of the stand-alone application CLUTO, designed to apply clustering methods to genomic information. Its first(More)