Interesting Subset Discovery and Its Application on Service Processes

Abstract

Various real-life datasets can be viewed as a set of records consisting of attributes explaining the records and set of measures evaluating the records. We address the problem of automatically discovering interesting subsets from such a dataset, such that the discovered interesting subsets have significantly different characteristics of performance than the rest of the dataset. We present an algorithm to discover such interesting subsets. The proposed algorithm uses a generic domain-independent definition of interestingness and uses various heuristics to intelligently prune the search space in order to build a solution scalable to large size datasets. We present application of the interesting subset discovery algorithm on four real-world case-studies and demonstrates the effectiveness of the interesting subset discovery algorithm in extracting insights in order to identify problem areas and provide improvement recommendations to wide variety of systems.