The use of knowledge in analogy and induction
The use of knowledge in analogy and induction
Exact learning Boolean functions via the monotone theory
Information and Computation
Concise, intelligible, and approximate profiling of multiple classes
International Journal of Human-Computer Studies - Special issue on Machine Discovery
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Turning CARTwheels: an alternating algorithm for mining redescriptions
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The levelwise version space algorithm and its application to molecular fragment finding
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Compositional mining of multirelational biological datasets
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining correlated subgraphs in graph databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Siren: an interactive tool for mining and visualizing geospatial redescriptions
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
From black and white to full color: extending redescription mining outside the Boolean world
Statistical Analysis and Data Mining
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We introduce a new data mining problem--redescription mining--that unifies considerations of conceptual clustering, constructive induction, and logical formula discovery. Redescription mining begins with a collection of sets, views it as a propositional vocabulary, and identifies clusters of data that can be defined in at least two ways using this vocabulary. The primary contributions of this paper are conceptual and theoretical: (i) we formally study the space of redescriptions underlying a dataset and characterize their intrinsic structure, (ii) we identify impossibility as well as strong possibility results about when mining redescriptions is feasible, (iii) we present several scenarios of how we can custom-build redescription mining solutions for various biases, and (iv) we outline how many problems studied in the larger machine learning community are really special cases of redescription mining. By highlighting its broad scope and relevance. we aim to establish the importance of redescription mining and make the case for a thrust in this new line of research.