caa a22 4500 44587127X CHVBK 20180317145512.0 cr unu---uuuuu 170323e20111101xx s 000 0 eng 10.1007/s10618-010-0209-3 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0209-3 De Bie Tijl Intelligent Systems Laboratory, University of Bristol, Bristol, UK aut Maximum entropy models and subjective interestingness: an application to tiles in binary databases [Elektronische Daten] [Tijl De Bie] Recent research has highlighted the practical benefits of subjective interestingness measures, which quantify the novelty or unexpectedness of a pattern when contrasted with any prior information of the data miner (Silberschatz and Tuzhilin, Proceedings of the 1st ACM SIGKDD international conference on Knowledge discovery and data mining (KDD95), 1995; Geng and Hamilton, ACM Comput Surv 38(3):9, 2006). A key challenge here is the formalization of this prior information in a way that lends itself to the definition of a subjective interestingness measure that is both meaningful and practical. In this paper, we outline a general strategy of how this could be achieved, before working out the details for a use case that is important in its own right. Our general strategy is based on considering prior information as constraints on a probabilistic model representing the uncertainty about the data. More specifically, we represent the prior information by the maximum entropy (MaxEnt) distribution subject to these constraints. We briefly outline various measures that could subsequently be used to contrast patterns with this MaxEnt model, thus quantifying their subjective interestingness. We demonstrate this strategy for rectangular databases with knowledge of the row and column sums. This situation has been considered before using computation intensive approaches based on swap randomizations, allowing for the computation of empirical p-values as interestingness measures (Gionis etal., ACM Trans Knowl Discov Data 1(3):14, 2007). We show how the MaxEnt model can be computed remarkably efficiently in this situation, and how it can be used for the same purpose as swap randomizations but computationally more efficiently. More importantly, being an explicitly represented distribution, the MaxEnt model can additionally be used to define analytically computable interestingness measures, as we demonstrate for tiles (Geerts etal., Proceedings of the 7th international conference on Discovery science (DS04), 2004) in binary databases. The Author(s), 2010 Maximum entropy principle nationallicence Subjective interestingness measures nationallicence Prior information nationallicence Rectangular databases nationallicence Swap randomizations nationallicence Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/3(2011-11-01), 407-446 1384-5810 23:3<407 2011 23 10618 https://doi.org/10.1007/s10618-010-0209-3 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0209-3 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- De Bie Tijl Intelligent Systems Laboratory, University of Bristol, Bristol, UK aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/3(2011-11-01), 407-446 1384-5810 23:3<407 2011 23 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer