Exploratory data mining has as its aim to assist a user in improving their understanding about the data. Considering this aim, it seems self-evident that in optimizing this process the data as well as the user need to be considered. Yet, the vast majority of exploratory data mining methods (including most methods for clustering, itemset and association rule mining, subgroup discovery, dimensionality reduction, etc) formalize interestingness of patterns in an objective manner, disregarding the user altogether. More often than not this leads to subjectively uninteresting patterns being reported. Here I will discuss a general mathematical framework for formalizing interestingness in a subjective manner. I will further demonstrate how it can be successfully instantiated for a variety of exploratory data mining problems. Finally, I will highlight some connections to other work, and outline some of the challenges and research opportunities ahead.
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