Abstract
Fuzzy Class Association Rules (FCARs) play an important role in decision support systems and have thus been extensively studied. Mining the important rules in FCARs becomes very difficult task, so Enhanced Equivalence Fuzzy Class Rule tree (EEFCR-tree) algorithm is proposed in this work. However, a major weakness of FCARs Miner is that when the number of constrained rules in a given class dominates the total constrained rules; its performance becomes slower than the normal method. To solve this problem this paper proposes a Proportion of Constraint Class Estimation (PPCE) algorithm for mining Enhanced Proportion Equivalence Fuzzy Constraint Class Association Rules (EPEFCARs) in order to save memory usage, run time and accuracy. Then, Proportion Frequency Occurrence count with Bat Algorithm (PFOCBA) is proposed for pruning rules which much satisfying the class constraints. Finally, an efficient algorithm is proposed for mining PEFCARs rules. Experimental results show that the proposed EPEFCR-tree algorithm is more efficient than Enhanced Equivalence Fuzzy Class Rule tree (EEFCR-tree), Novel Equivalence Fuzzy Class Rule tree (NECR-tree) Miner results are measured in terms of run time, accuracy and memory usage. Experiments show that the proposed method is faster than existing methods.
Highlights
Association Rule Mining (ARM) is widely analysed because of its application in several areas for instance market basket analysis, protein sequencing, medicine, census data processing, and fraud detection
It is eminent that the nodes, which could not produce constrained rules as identified from Theorem 3 are detached from Lr .At that point, the procedure ConstraintEPEFCR -Miner is called with the parameters Lr,minFSup, minFConf, Constraint Class (ConC) and probability Pri to mine each and every constrained EPEFCR from dataset D
Proportion Frequency Occurrence count with Bat algorithm (PFOCBA) is presented for rule pruning step, which removes redundant or noisy information enclosed in the rule set and chooses a subset of higher quality Proportion Equivalence Fuzzy Constraint Class Association Rules (PEFCARs)
Summary
Association Rule Mining (ARM) is widely analysed because of its application in several areas for instance market basket analysis, protein sequencing, medicine, census data processing, and fraud detection. Nguyen and Nguyen [14] proposed a new effective pruning method to construct a quicker classifier dependent upon CARs. Initially, create a form named Lattice of Equivalence Class Rules (LECR) and present a technique for fast mining CARs. Secondly, present a technique to prune rules, which are redundant in LECR. Gonzales et al [16] presented a novel post-processing technique for pruning CARs by a grouping of data and an evolutionary technique called Genetic Relation Technique (GRA) (4) Proposed EPEFCR-tree technique is very capable when compared to Enhanced Equivalence Fuzzy Class Rule tree (EEFCR-tree), Novel Equivalence Fuzzy Class Rule tree (NECR-tree) Miner in regard to run time, accurateness and memory usage
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