Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Assessing data mining results via swap randomization
ACM Transactions on Knowledge Discovery from Data (TKDD)
MINI: Mining Informative Non-redundant Itemsets
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
The Chosen Few: On Identifying Valuable Patterns
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Maximum entropy based significance of itemsets
Knowledge and Information Systems
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Tell me something I don't know: randomization strategies for iterative data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining interesting sets and rules in relational databases
Proceedings of the 2010 ACM Symposium on Applied Computing
Maximum entropy models and subjective interestingness: an application to tiles in binary databases
Data Mining and Knowledge Discovery
An information theoretic framework for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge discovery interestingness measures based on unexpectedness
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Data summarization for network traffic monitoring
Journal of Network and Computer Applications
Interesting pattern mining in multi-relational data
Data Mining and Knowledge Discovery
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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This paper suggests a framework for mining subjectively interesting pattern sets that is based on two components: (1) the encoding of prior information in a model for the data miner's state of mind; (2) the search for a pattern set that is maximally informative while efficient to convey to the data miner. We illustrate the framework with an instantiation for tile patterns in binary databases where prior information on the row and column marginals is available. This approach implements step (1) above by constructing the MaxEnt model with respect to the prior information [2, 3], and step (2) by relying on concepts from information and coding theory. We provide a brief overview of a number of possible extensions and future research challenges, including a key challenge related to the design of empirical evaluations for subjective interestingness measures.