C4.5: programs for machine learning
C4.5: programs for machine learning
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Rule Evaluation Measures: A Unifying View
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
ART: A Hybrid Classification Model
Machine Learning
Mining border descriptions of emerging patterns from dataset pairs
Knowledge and Information Systems
A New Association Rule-Based Text Classifier Algorithm
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
On Mining Instance-Centric Classification Rules
IEEE Transactions on Knowledge and Data Engineering
Spatial associative classification: propositional vs structural approach
Journal of Intelligent Information Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
A Novel Rule Weighting Approach in Classification Association Rule Mining
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Boosting text segmentation via progressive classification
Knowledge and Information Systems
A Novel Rule Ordering Approach in Classification Association Rule Mining
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Associative classification of mammograms using weighted rules
Expert Systems with Applications: An International Journal
Direct Discriminative Pattern Mining for Effective Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Integration of heterogeneous models to predict consumer behavior
Expert Systems with Applications: An International Journal
Classifying Using Specific Rules with High Confidence
MICAI '10 Proceedings of the 2010 Ninth Mexican International Conference on Artificial Intelligence
Practical application of associative classifier for document classification
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Threshold tuning for improved classification association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining frequent patterns and association rules using similarities
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Association rule mining and classification are important tasks in data mining. Using association rules has proved to be a good approach for classification. In this paper, we propose an accurate classifier based on class association rules (CARs), called CAR-IC, which introduces a new pruning strategy for mining CARs, which allows building specific rules with high confidence. Moreover, we propose and prove three propositions that support the use of a confidence threshold for computing rules that avoids ambiguity at the classification stage. This paper also presents a new way for ordering the set of CARs based on rule size and confidence. Finally, we define a new coverage strategy, which reduces the number of non-covered unseen-transactions during the classification stage. Results over several datasets show that CAR-IC beats the best classifiers based on CARs reported in the literature.