A Method for Query Top-K Rules from Class Association Rule Set

2016 ◽  
pp. 644-653
Author(s):  
Loan T. T. Nguyen ◽  
Hai T. Nguyen ◽  
Bay Vo ◽  
Ngoc-Thanh Nguyen
Keyword(s):  
Association Rule ◽  
Rule Set ◽  
Class Association Rule

scholarly journals Tendency Mining in Dynamic Association Rules Based on SVM Classifier

2014 ◽  
Vol 8 (1) ◽  
pp. 303-307 ◽  
Author(s):  
Zhonglin Zhang ◽  
Zongcheng Liu ◽  
Chongyu Qiao
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Feature Vector ◽  
Svm Classifier ◽  
Complex Data ◽  
Traditional Methods ◽  
Rule Set ◽  
Lower Complexity ◽  
Dynamic Association ◽  
Class Association Rule

A method of tendency mining in dynamic association rule based on compatibility feature vector SVM classifier is proposed. Firstly, the class association rule set named CARs is mined by using the method of tendency mining in dynamic association rules. Secondly, the algorithm of SVM is used to construct the classifier based on compatibility feature vector to classify the obtained CARs taking advantage when dealing with high complex data. It uses a method based on judging rules’ weight to construct the model. At last, the method is compared with the traditional methods with respect to the mining accuracy. The method can solve the problem of high time complexity and have a higher accuracy than the traditional methods which is helpful to make mining dynamic association rules more accurate and effective. By analyzing the final results, it is proved that the method has lower complexity and higher classification accuracy.

scholarly journals Mining the optimal class association rule set

2002 ◽  
Vol 15 (7) ◽  
pp. 399-405 ◽  
Author(s):  
Jiuyong Li ◽  
Hong Shen ◽  
Rodney Topor
Keyword(s):  
Association Rule ◽  
Rule Set ◽  
Class Association Rule

scholarly journals Classification of Pruning Methodologies for Model Development using Data Mining Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 2043-2047 ◽  
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Model Development ◽  
Data Mining Techniques ◽  
Rule Set ◽  
Using Data ◽  
Rule Pruning ◽  
Class Association Rule

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..

Analysis of Various Interestingness Measures in Class Association Rule Mining

2011 ◽  
Vol 4 (4) ◽  
pp. 295-304 ◽  
Author(s):  
Xianneng LI ◽  
Shingo MABU ◽  
Huiyu ZHOU ◽  
Kaoru SHIMADA ◽  
Kotaro HIRASAWA
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Interestingness Measures ◽  
Class Association Rule

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

Rare Class Association Rule Mining with Multiple Imbalanced Attributes

2010 ◽  
pp. 66-75 ◽  
Author(s):  
Huaifeng Zhang ◽  
Yanchang Zhao ◽  
Longbing Cao ◽  
Chengqi Zhang ◽  
Hans Bohlscheid
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Uniform Space ◽  
Rule Mining ◽  
Target Class ◽  
Left Hand ◽  
The Social ◽  
Rare Class ◽  
Class Association Rule ◽  
The Right

In this chapter, the authors propose a novel framework for rare class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This algorithm is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through a standard algorithm while the rules with imbalanced attributes are mined based on newly defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied in the social security field. Although some attributes are severely imbalanced, rules with a minority of imbalanced attributes have been mined efficiently.

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