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
Fast sequential and parallel algorithms for association rule mining: a comparison
Fast sequential and parallel algorithms for association rule mining: a comparison
Machine Learning - Special issue on learning with probabilistic representations
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient GA Based Techniques for Classification
Applied Intelligence
Fuzzy Neural Network Models for Classification
Applied Intelligence
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Scoring the Data Using Association Rules
Applied Intelligence
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
ECML '93 Proceedings of the European Conference 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
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
ART: A Hybrid Classification Model
Machine Learning
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Dilated chi-square: a novel interestingness measure to build accurate and compact decision list
Intelligent information processing II
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
A new approach to classification based on association rule mining
Decision Support Systems
Online mining of fuzzy multidimensional weighted association rules
Applied Intelligence
On pruning and tuning rules for associative classifiers
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Multimodal classification: case studies
Transactions on Rough Sets V
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
Lattice based associative classifier
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Data stream classification with artificial endocrine system
Applied Intelligence
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
Multi-level rough set reduction for decision rule mining
Applied Intelligence
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Associative classification has aroused significant research attention in recent years due to its advantage in rule forms with satisfactory accuracy. However, the rules in associative classifiers derived from typical association rule mining (e.g., Apriori-type) may easily become too many to be understood and even be sometimes redundant or conflicting. To deal with these issues of concern, a recently proposed approach (i.e., GARC) appears to be superior to other existing approaches (e.g., C4.5-type, NN, SVM, CBA) in two respects: one is its classification accuracy that is equally satisfactory; the other is the compactness that the generated classifier is constituted with much fewer rules. Along with this line of methodological thinking, this paper presents a novel GARC-type approach, namely GEAR, to build an associative classifier with three distinctive and desirable features. First, the rules in the GEAR classifier are more intuitively appealing; second, the GEAR classification accuracy is improved or at least as good as others; and third, the GEAR classifier is significantly more compact in size. In doing so, a number of notions including rule redundancy and compact set are provided, together with related properties that could be incorporated into the rule mining process as algorithmic pruning strategies. The experimental results with benchmarking datasets also reveal that GEAR outperforms GARC and other approaches in an effective manner.