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For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each(More)
Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLAHANS which is based on randomized search. We also develop two spatial(More)
This paper deals with nding outliers (exceptions) in large, multidimensional datasets. The iden-tiication of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce , credit card fraud, and even the analysis of performance statistics of professional athletes. Existing methods that we have seen for nding outliers(More)
This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The identification of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. Existing methods that we have seen for finding(More)
—Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. To this end, this paper has three main contributions. First, we propose a new clustering method called CLARANS, whose aim is to identify spatial structures that may be present in the data. Experimental results indicate that,(More)
—Software developers are often faced with modification tasks that involve source which is spread across a code base. Some dependencies between source code, such as those between source code written in different languages, are difficult to determine using existing static and dynamic analyses. To augment existing analyses and to help developers identify(More)
From the standpoint of supporting human-centered discovery of knowledge, the present-day model of mining association rules suuers from the following serious shortcomings: (i) lack of user exploration and control, (ii) lack of focus, and (iii) rigid notion of relationships. In eeect, this model functions as a black-box, admitting little user interaction in(More)