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
Mining fuzzy association rules for classification problems
Computers and Industrial Engineering
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
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
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Using association rules to make rule-based classifiers robust
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
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
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
Building classification rules for case-based classifier using fuzzy sets and formal concept analysis
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Fuzzy multiple support associative classification approach for prediction
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
A parsimony fuzzy rule-based classifier using axiomatic fuzzy set theory and support vector machines
Information Sciences: an International Journal
Fuzzy association rule mining approaches for enhancing prediction performance
Expert Systems with Applications: An International Journal
Fuzzy generalised classifier for distributed knowledge discovery
International Journal of Business Intelligence and Data Mining
| Hi-index | 12.05 |
Classification is one of the most popular data mining techniques applied to many scientific and industrial problems. The efficiency of a classification model is evaluated by two parameters, namely the accuracy and the interpretability of the model. While most of the existing methods claim their accurate superiority over others, their models are usually complex and hardly understandable for the users. In this paper, we propose a novel classification model that is based on easily interpretable fuzzy association rules and fulfils both efficiency criteria. Since the accuracy of a classification model can be largely affected by the partitioning of numerical attributes, this paper discusses several fuzzy and crisp partitioning techniques. The proposed classification method is compared to 15 previously published association rule-based classifiers by testing them on five benchmark data sets. The results show that the fuzzy association rule-based classifier presented in this paper, offers a compact, understandable and accurate classification model.