Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Structures for Range Searching
ACM Computing Surveys (CSUR)
ACM Transactions on Information Systems (TOIS)
Generalizing the notion of support
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast discovery of unexpected patterns in data, relative to a Bayesian network
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Finding regional co-location patterns for sets of continuous variables in spatial datasets
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
An association analysis approach to biclustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Tight correlated item sets and their efficient discovery
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Mining bi-sets in numerical data
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Minimum variance associations: discovering relationships in numerical data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A formal and empirical analysis of the fuzzy gamma rank correlation coefficient
Information Sciences: an International Journal
Coupled attribute analysis on numerical data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We study the mining of interesting patterns in the presence of numerical attributes. Instead of the usual discretization methods, we propose the use of rank based measures to score the similarity of sets of numerical attributes. New support measures for numerical data are introduced, based on extensions of Kendall's tau, and Spearman's Footrule and rho. We show how these support measures are related. Furthermore, we introduce a novel type of pattern combining numerical and categorical attributes. We give efficient algorithms to find all frequent patterns for the proposed support measures, and evaluate their performance on real-life datasets.