원문정보
초록
영어
Association rule mining which is the most significance and use is one of a relevant approach for data mining. The fundamental of the association rule mining approach have been Apriori and introduce many access with changes in the apriori but though main idea continue to be the same that is use of support and confidence threshold (s). Conforming to the theory it is well know that no work has been done in the domain of Enhancing pruning step of Apriori. This paper introduces a new algorithm M-APRIORI. This algorithm advances to Enhance the Apriori algorithm by using mean support (supmean) rather than minimum support (supmin), to produce probable item-set instead of large item-set and Artificial bee Colony technique used to optimization the rules. In this paper Apriroi and M-Apriori are based On Artificial Bee Colony.
목차
1. Introduction
1.1 Association Rule Mining
1.2 Artificial Beef Colony
1.3 APRIORI Algorithm
2. Proposed Approach (M-APRIORI)
2.1 M-APRIORI Algorithm Explanation
2.2 Designing Artificial Bee Colony (ABC) Optimization for Apriori & M-Aprior
3. Experimental Results
3.1 SMOKING DATASET
3.2 Weather Dataset
4. Conclusion
References