Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Machine Learning and Its Applications
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
An implementation of the FP-growth algorithm
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Quality Measures in Data Mining (Studies in Computational Intelligence)
Quality Measures in Data Mining (Studies in Computational Intelligence)
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
String analysis by sliding positioning strategy
Journal of Computer and System Sciences
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In this paper we present a novel method to detect interesting patterns in strings. A common way to refine results of pattern mining algorithms is using interestingness measures. But the set of appropiate measures is different in each domain and problem. The aim of our research is to obtain a model that classify patterns by interest. The method is based on the application of machine learning algorithms to a generated dataset from factors features. Each dataset row is associated to a factor of a string and contains values of different interestingness measures and contextual information. We also propose a new interestingness measure based on an entropy principle which improves obtained classification results. The proposed method avoids the experts having to configure parameters in order to obtain interesting patterns. We demonstrated the utility of the method by giving example results on real data. The datasets and scripts to reproduce experiments are available on-line.