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Subjective Measures and their Role in Data Mining Process

Knowledge Discovery in Databases (KDD) is the process of extracting previously unknown, hidden and interesting patterns from a huge amount of data stored in databases. Data mining is a stage of the entire KDD process that involves applying a particular data mining algorithm to extract an interesting knowledge. One of the very important aspects of any data mining task is the evaluation process of the discovered knowledge. Furthermore, the major issue that faces data mining community is how to use our existing knowledge about domain to evaluate the discovered patterns. For the patterns to be interesting, the user has to be involved by providing his/her prior knowledge about domain. While objective measures can be quantified by using statistical methods, subjective measures are determined based on the user understandability of the domain. Use of objective measures of interestingness in popular data mining algorithms often leads to another data mining problem, although of reduced complexity. The reduction in the volume of the discovered patterns is desirable in order to improve the efficiency of the overall KDD process. Subjective measures of interestingness are required to achieve this. In this paper we study the subjective interestingness of the discovered patterns and show their role in extracting novel and interesting knowledge.