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Contrast Data Mining: Concepts, Algorithms, and Applications
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This paper defines the cross domain similarity mining (CDSM) problem, and motivates CDSM with several potential applications. CDSM has big potential in (1) supporting understanding transfer and (2) supporting research by analogy, since similarity is vital to understanding/meaning and to identifying analogy, and since analogy is a fundamental approach frequently used in hypothesis generation and in research. CDSM also has big potential in (3) advancing learning transfer since cross domain similarities can shed light on how to best adapt classifiers/clusterings across given domains and how to avoid negative transfer. CDSM can also be useful for (4) solving the schema/ontology matching problem. Moreover, this paper gives a list of potential research questions for CDSM, and compares CDSM with related studies. One purpose of this paper is to introduce the CDSM problem to the wide KDD community in order to quickly realize the full potential of CDSM.