Machine Learning - Special issue on learning with probabilistic representations
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
C4.5: Programs for Machine Learning
C4.5: Programs for 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
Efficient Automated Mining of Fuzzy Association Rules
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
One-class svms for document classification
The Journal of Machine Learning Research
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
A review of associative classification mining
The Knowledge Engineering Review
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Elicitation of fuzzy association rules from positive and negative examples
Fuzzy Sets and Systems
I-FAC: Efficient Fuzzy Associative Classifier for Object Classes in Images
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Fuzzy versus quantitative association rules: a fair data-driven comparison
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
In Defense of Fuzzy Association Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
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Associative Classification leverages Association Rule Mining (ARM) to train Rule-based classifiers. The classifiers are built on high quality Association Rules mined from the given dataset. Associative Classifiers are very accurate because Association Rules encapsulate all the dominant and statistically significant relationships between items in the dataset. They are also very robust as noise in the form of insignificant and low-frequency itemsets are eliminated during the mining and training stages. Moreover, the rules are easy-to-comprehend, thus making the classifier transparent. Conventional Associative Classification and Association Rule Mining (ARM) algorithms are inherently designed to work only with binary attributes, and expect any quantitative attributes to be converted to binary ones using ranges, like "Age = [25, 60]". In order to mitigate this constraint, Fuzzy logic is used to convert quantitative attributes to fuzzy binary attributes, like "Age = Middle-aged", so as to eliminate any loss of information arising due to sharp partitioning, especially at partition boundaries, and then generate Fuzzy Association Rules using an appropriate Fuzzy ARM algorithm. These Fuzzy Association Rules can then be used to train a Fuzzy Associative Classifier. In this paper, we also show how Fuzzy Associative Classifiers so built can be used in a wide variety of domains and datasets, like transactional datasets and image datasets.