Comparative analysis of different techniques in classification based on association rules

Vishwakarma, Nitendra Kumar; Agarwal, Jitendra; Agarwal, Shikha; Sharma, Sanjeev

doi:10.1109/iccic.2013.6724136

Cited by 4 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It significantly reduces the size of the class association rule set and minimizes rules redundancy. The algorithms, including [5,7] have used the pruning of redundant class association rules. It perform pruning immediately as a rule is inserted into the data structure called CR-tree.…”

Section:  Removing Redundant Class Association Rulesmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Pruning Methodologies for Model Development using Data Mining Techniques

Pal,

Pathak,

Yadav

et al. 2019

IJEAT

View full text Add to dashboard Cite

Knowledge discovery process deals with two essential data mining techniques, association and classification. Classification produces a set of large number of associative classification rules for a given observation. Pruning removes unnecessary class association rules without losing classification accuracy. These processes are very significant but at the same time very challenging. The experimental results and limitations of existing class association rules mining techniques have shown that there is a requirement to consider more pruning parameters so that the size of classifier can be further optimized. Here through this paper we are presenting a survey various strategies for class association rule pruning and study their effects that enables us to extract efficient compact and high confidence class association rule set and we have also proposed a pruning methodology..

show abstract

Section:  Removing Redundant Class Association Rulesmentioning

confidence: 99%

“…Conflicting class association rules refers to such rules that have similar LHS item-sets but predicting different classes in RHS. Let's take given rules such as R  C1 and R  C2 [7]. Proposed a pruning technique considers these conflicting rules and removes them.…”

Section:  Handling Conflicting Class Association Rulesmentioning

confidence: 99%

Classification of Pruning Methodologies for Model Development using Data Mining Techniques

Pal,

Pathak,

Yadav

et al. 2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…Associative Classification (AC) (Vishwakarma et al 2013;Phan-Luong, 2013) is an integration of association and classification methods aims to build a classifier describing the classes of the input training data set. Class Association Rules (CARs) (Mabu et al 2011) are basically used to build a classification model for which prediction defines the relationship between the itemsets and the class labels.…”

Section: Introduction 11 Associative Classificationmentioning

confidence: 99%

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

Subbulakshmi¹,

Deisy²,

Parthasarathy³

2023

International Journal of Information Retrieval Research

View full text Add to dashboard Cite

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.

show abstract

Extraction of the association rules from artificial neural networks based on the multiobjective optimization

Yedjour

Chouraqui

2022

Network: Computation in Neural Systems

View full text Add to dashboard Cite

Comparative analysis of different techniques in classification based on association rules

Cited by 4 publications

References 12 publications

Classification of Pruning Methodologies for Model Development using Data Mining Techniques

Classification of Pruning Methodologies for Model Development using Data Mining Techniques

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

Extraction of the association rules from artificial neural networks based on the multiobjective optimization

Contact Info

Product

Resources

About