Introduction to algorithms
Elements of information theory
Elements of information theory
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Axiomatic Approach to Feature Subset Selection Based on Relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Propositionalization approaches to relational data mining
Relational Data Mining
ECML '95 Proceedings of the 8th European Conference on Machine Learning
A Practical Approach to Feature Selection
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Finding low-entropy sets and trees from binary data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining non-redundant high order correlations in binary data
Proceedings of the VLDB Endowment
Mining Entropy l-Diversity Patterns
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
An Improved Algorithm for Mining Non-Redundant Interacting Feature Subsets
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Efficient algorithms for mining constrained frequent patterns from uncertain data
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Efficient algorithms for the mining of constrained frequent patterns from uncertain data
ACM SIGKDD Explorations Newsletter
Pattern selection problems in multivariate time-series using equation discovery
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Optimal constraint-based decision tree induction from itemset lattices
Data Mining and Knowledge Discovery
Guest Editorial: Global modeling using local patterns
Data Mining and Knowledge Discovery
Discovering highly informative feature sets from data streams
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Summarising data by clustering items
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Summarizing categorical data by clustering attributes
Data Mining and Knowledge Discovery
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In this paper we present a new approach to mining binary data. We treat each binary feature (item) as a means of distinguishing two sets of examples. Our interest is in selecting from the total set of items an itemset of specified size, such that the database is partitioned with as uniform a distribution over the parts as possible. To achieve this goal, we propose the use of joint entropy as a quality measure for itemsets, and refer to optimal itemsets of cardinality k as maximally informative k-itemsets. We claim that this approach maximises distinctive power, as well as minimises redundancy within the feature set. A number of algorithms is presented for computing optimal itemsets efficiently.