C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Improving an Association Rule Based Classifier
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
Compact fuzzy association rule-based classifier
Expert Systems with Applications: An International Journal
CSMC: A combination strategy for multi-class classification based on multiple association rules
Knowledge-Based Systems
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The fact of building an accurate classification and prediction system remains one of the most significant challenges in knowledge discovery and data mining. In this paper, a Knowledge Discovery (KD) framework is proposed; based on the integrated fuzzy approach, more specifically Fuzzy C-Means (FCM) and the new Multiple Support Classification Association Rules (MSCAR) algorithm. MSCAR is considered as an efficient algorithm for extracting both rare and frequent rules using vertical scanning format for the database. Consequently, the adaptation of such a process sufficiently minimized the prediction error. The experimental results regarding two data sets; Abalone and road traffic, show the effectiveness of the proposed approach in building a robust prediction system. The results also demonstrate that the proposed KD framework outperforms the existing prediction systems.