A Customizable Hybrid Approach to Data Clustering

Most current data clustering algorithms in data mining are based on a distance calculation in certain metric space. For Spatial Database Systems (SDBS), the Euclidean distance between two data points is often used to represent the relationship between data points. However, in some spatial settings and many other applications, distance alone is not enough to represent all the attributes of the relation between data points. We need a more powerful model to record more relational information between data objects. This paper adopts a graph model by which a database is regarded as a graph: each vertex of the graph represents a data point, and each edge, weighted or unweighted, is used to record the relation between two data points connected by the edge. Based on the graph model, this paper presents a set of cluster analysis criteria to guide data clustering. The criteria can be used to measure clustering results and help improving the quality of clustering. Further, a customizable algorithm using the criteria is proposed and implemented. This algorithm can produce clusters according to users ’ specifications. Preliminary experiments show encouraging results. 1