2019-08-20 15:17 association rule mining and classical Apriori mining algorithms for gridbased knowledge discovery. Qiang et al. , [13 proposed association classification based method on compactness of rules. The proposed approach suffers from a difficulty of over fitting because the classification rules
The ClassAssociation Rule (CAR) is a kind of classification rule. Its purpose is mining rules that satisfy minimum support (minSup) and minimum confidence (minConf) thresholds. Liu et al. (1998) first proposed a method for mining CARs. association classification based on compactness of rules
The first association rulebased classifiers is CBA (Classification Based on Associations), proposed by Liu, Hsu, and Ma (1998), generates all of the classification association rules (CARs) that satisfy userdefined support and confidence thresholds. CARs with high confidence values are selected to form the classifier.
(Classification based on Predictive Association Rules), which combines the advantages of both associative classification and traditional rulebased classification. NIU Qiang et al. [15 propose a new association classification method based on compactness of rules. It extends Apriori Algorithm, which association classification based on compactness of rules
Besides other techniques for data classification such as decision tree induction, Bayesian classification, neural network, classification based on data warehousing technology, and etc, the associative classification or classification based on association rules is an integrated technique that applies the methods of association rule mining to the classification. Associative classification has high classification accuracy and strong flexibility. However, it still suffers from overfitting since the classification rules satisfied both minimum support and In this paper, we propose a new association classification method based on compactness of rules, it extends Apriori [Show full abstract Algorithm which considers the interestingness association classification based on compactness of rules The proposed algorithm is called algorithm CBA (Classification Based on Associations). It consists of two parts, a rule generator (called CBARG), which is based on algorithm Apriori for finding association rules in (Agrawal and Srikant 1994), and a classifier builder (called CBACB). This section discusses CBARG. However, it still suffers from overfitting since the classification rules satisfied both minimum support and minimum confidence are returned as strong association rules back to the classifier. In this paper, we propose a new association classification method based on compactness of rules, it extends Apriori Algorithmwhich considers the interestingness, importance, overlapping relationships among rules. Associative classification (AC) is a data mining approach that combines association rule and classification to build classification models (classifiers). AC has attracted a significant attention from several researchers mainly because it derives accurate classifiers that contain simple yet effective rules. classification based on association rules techniques is surveyed and discussed, which extends the basic idea of association rule [1 and integrates it with classification to generate a subset of effective rules. This proposal uses association rule mining approach in the classification framework. It has been named multiclass
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