document: Towards systematic design of distance functions for data mining applications Distance function computation is a key subtask in many data mining algorithms and applications . The most effective form of the distance function can only be expressed in the context of a particular data domain . It is also often a challenging and non-trivial task to find the most effective form of the distance function . For example , in the text domain , distance function design has been considered such an important and complex issue that it has been the focus of intensive research over three decades . The final design of distance functions in this domain has been reached only by detailed empirical testing and consensus over the quality of results provided by the different variations . With the increasing ability to collect data in an automated way , the number of new kinds of data continues to increase rapidly . This makes it increasingly difficult to undertake such efforts for each and every new data type . The most important aspect of distance function design is that since a human is the end-user for any application , the design must satisfy the user requirements with regard to effectiveness . This creates the need for a systematic framework to design distance functions which are sensitive to the particular characteristics of the data domain . In this paper , we discuss such a framework . The goal is to create distance functions in an automated waywhile minimizing the work required from the user . We will show that this framework creates distance functions which are significantly more effective than popularly used functions such as the Euclidean metric .
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