Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for 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
The nature of statistical learning theory
The nature of statistical learning theory
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Negative Association Rules
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
A Lazy Approach to Pruning Classification Rules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On the Mining of Substitution Rules for Statistically Dependent Items
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Associative text categorization exploiting negated words
Proceedings of the 2006 ACM symposium on Applied computing
CCCS: a top-down associative classifier for imbalanced class distribution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning rules with negation for text categorization
Proceedings of the 2007 ACM symposium on Applied computing
Exclusion-inclusion based text categorization of biomedical articles
Proceedings of the 2007 ACM symposium on Document engineering
A review of associative classification mining
The Knowledge Engineering Review
Compact fuzzy association rule-based classifier
Expert Systems with Applications: An International Journal
ACM SIGKDD Explorations Newsletter
A Novel Algorithm for Associative Classification
Neural Information Processing
A new sampling technique for association rule mining
Journal of Information Science
Constructing Associative Classifiers from Decision Tables
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Mining correct properties in incomplete databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
CIMDS: adapting postprocessing techniques of associative classification for malware detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Mining associative classification rules with stock trading data - A GA-based method
Knowledge-Based Systems
Re-mining positive and negative association mining results
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Classification inductive rule learning with negated features
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Re-mining item associations: Methodology and a case study in apparel retailing
Decision Support Systems
ISIICT'09 Proceedings of the Third international conference on Innovation and Information and Communication Technology
Lattice based associative classifier
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Mining actionable behavioral rules
Decision Support Systems
X-Class: Associative Classification of XML Documents by Structure
ACM Transactions on Information Systems (TOIS)
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
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Associative classifiers use association rules to associate attribute values with observed class labels. This model has been recently introduced in the literature and shows good promise. The proposals so far have only concentrated on, and differ only in the way rules are ranked and selected in the model. We propose a new framework that uses different types of association rules, positive and negative. Negative association rules of interest are rules that either associate negations of attribute values to classes or negatively associate attribute values to classes. In this paper we propose a new algorithm to discover at the same time positive and negative association rules. We introduce a new associative classifier that takes advantage of these two types of rules. Moreover, we present a new way to prune irrelevant classification rules using a correlation coefficient without jeopardizing the accuracy of our associative classifier model. Our preliminary results with UCI datasets are very encouraging.