Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
On the approximation of curves by line segments using dynamic programming
Communications of the ACM
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Turning CARTwheels: an alternating algorithm for mining redescriptions
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Reasoning about sets using redescription mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Redescription mining: algorithms and applications in bioinformatics
Redescription mining: algorithms and applications in bioinformatics
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Cross-Mining Binary and Numerical Attributes
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
The Journal of Machine Learning Research
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
On subgroup discovery in numerical domains
Data Mining and Knowledge Discovery
Redescription mining: structure theory and algorithms
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Randomization methods for assessing data analysis results on real-valued matrices
Statistical Analysis and Data Mining
Maximal exceptions with minimal descriptions
Data Mining and Knowledge Discovery
Siren: an interactive tool for mining and visualizing geospatial redescriptions
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Redescription mining is a powerful data analysis tool that is used to find multiple descriptions of the same entities. Consider geographical regions as an example. They can be characterized by the fauna that inhabits them on one hand and by their meteorological conditions on the other hand. Finding such redescriptors, a task known as niche-finding, is of much importance in biology. Current redescription mining methods cannot handle other than Boolean data. This restricts the range of possible applications or makes discretization a pre-requisite, entailing a possibly harmful loss of information. In niche-finding, while the fauna can be naturally represented using a Boolean presence/absence data, the weather cannot. In this paper, we extend redescription mining to categorical and real-valued data with possibly missing values using a surprisingly simple and efficient approach. We provide extensive experimental evaluation to study the behavior of the proposed algorithm. Furthermore, we show the statistical significance of our results using recent innovations on randomization methods. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 (Part of this work was done when the author was with HIIT.)