Incremental constraint class association rule mining of student performance dataset

Subbulakshmi, B.; Monisha, M.

doi:10.1109/icrtit.2016.7569583

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…An incremental algorithm called ICCAR proposed in Subbulakshmi & Monisha (2016) is used for generating CCARs from incremental data. The proposed algorithm makes use of a tree structure called Incremental Constraint Class Rule-tree (ICCR-tree) (Subbulakshmi & Monisha 2016) for incremental data.…”

Section: Mining Ccars Using Incremental Ccar Algorithmmentioning

confidence: 99%

A Novel Incremental Framework for Building Classifier Using Constraint Class Association Rules

Subbulakshmi¹,

Deisy²,

Parthasarathy³

2023

International Journal of Information Retrieval Research

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Associative classification (AC) performs much better than other traditional classifiers. It generates a huge number of class association rules (CARs). Since users are interested in the subset of rules, constraints are introduced in the generation of CARs. Real-world databases are record-based in which data is continuously added which demands incremental mining. Hence, constraint class association rules (CCAR) is mined from incremental data. To limit the number of rules and to remove the duplicate rules, redundant rule pruning and duplicate rule pruning techniques are applied. To improve the accuracy of the classifier, the rule selection using principality metric has been applied and the classifier is constructed with rules possessing high principality. Then, classifier is evaluated using single rule and multiple rule prediction methods and the accuracy of the proposed classifier are measured. Experimental results show that the accuracy of the proposed classifier is relatively higher when compared to other algorithms.

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Section: Mining Ccars Using Incremental Ccar Algorithmmentioning

confidence: 99%

A Novel Incremental Framework for Building Classifier Using Constraint Class Association Rules

Subbulakshmi¹,

Deisy²,

Parthasarathy³

2023

International Journal of Information Retrieval Research

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“…Subbulakshmi et al [18] utilizes Incremental Constraint Class Association Rule (ICCAR) Technique by building Incremental Constraint Class Rule (ICCR) Tree to produce the Constraint Class Association Rules. According to incremental data, the ICCR Tree is brought up to date for the novel set of records deprived of rescanning the dataset, it takes less memory while matched up with the previous associative classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

Proportion frequency occurrence count with bat algorithm (FOCBA) for rule optimization and mining of proportion equivalence fuzzy constraint class association rules (PEFCARs)

Ramesh¹,

Saravanan²

2018

PEN

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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 (EEFCRtree), 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.

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Incremental constraint class association rule mining of student performance dataset

Cited by 2 publications

References 5 publications

A Novel Incremental Framework for Building Classifier Using Constraint Class Association Rules

A Novel Incremental Framework for Building Classifier Using Constraint Class Association Rules

Proportion frequency occurrence count with bat algorithm (FOCBA) for rule optimization and mining of proportion equivalence fuzzy constraint class association rules (PEFCARs)

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