An Evolutionary Method for Exceptional Association Rule Set Discovery from Incomplete Database

2014 ◽  
pp. 133-147 ◽  
Author(s):  
Kaoru Shimada ◽  
Takashi Hanioka
Keyword(s):  
Association Rule ◽  
Evolutionary Method ◽  
Rule Set

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 Privacy preserving association rule hiding using border based approach

2021 ◽  
Vol 23 (2) ◽  
pp. 1137
Author(s):  
Suma B. ◽  
Shobha G.
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Sensitive Information ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Large Databases ◽  
Hidden Correlations ◽  
Rule Set

<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

Exceptional Association Rule Set Discovery from Community-Dwelling Elderly People Database

2018 ◽  
Author(s):  
Kaoru Shimada ◽  
Hisae Aoki ◽  
Keiko Kubota ◽  
Satoru Haresaku ◽  
Shinsuke Mizutani ◽  
...  
Keyword(s):  
Elderly People ◽  
Association Rule ◽  
Community Dwelling ◽  
Rule Set

MINING NON-REDUNDANT ASSOCIATION RULES BASED ON CONCISE BASES

2007 ◽  
Vol 21 (04) ◽  
pp. 659-675 ◽  
Author(s):  
YUE XU ◽  
YUEFENG LI
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Mining Community ◽  
Rule Set ◽  
Closed Itemsets ◽  
Minimal Generators

Association rule mining has many achievements in the area of knowledge discovery. However, the quality of the extracted association rules has not drawn adequate attention from researchers in data mining community. One big concern with the quality of association rule mining is the size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant, thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a reliable exact association rule basis from which more concise nonredundant rules can be extracted. We prove that the redundancy eliminated using the proposed reliable association rule basis does not reduce the belief to the extracted rules. Moreover, this paper proposes a level wise approach for efficiently extracting closed itemsets and minimal generators — a key issue in closure based association rule mining.

scholarly journals Detection of Fuzzy Association Rules by Fuzzy Transforms

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Ferdinando Di Martino ◽  
Salvatore Sessa
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Data Association ◽  
New Method ◽  
Coarse Grained ◽  
Fuzzy Transform ◽  
Fuzzy Partition ◽  
Fuzzy Association Rules ◽  
Rule Set ◽  
Confidence Index

We present a new method based on the use of fuzzy transforms for detecting coarse-grained association rules in the datasets. The fuzzy association rules are represented in the form of linguistic expressions and we introduce a pre-processing phase to determine the optimal fuzzy partition of the domains of the quantitative attributes. In the extraction of the fuzzy association rules we use the AprioriGen algorithm and a confidence index calculated via the inverse fuzzy transform. Our method is applied to datasets of the 2001 census database of the district of Naples (Italy); the results show that the extracted fuzzy association rules provide a correct coarse-grained view of the data association rule set.

scholarly journals An Algorithm for Generating a Recommended Rule Set Based on Learner's Browse Interest

2018 ◽  
Vol 13 (11) ◽  
pp. 102
Author(s):  
Xiaowei Hao ◽  
Shanshan Han
Keyword(s):  
Association Rule ◽  
Quantitative Relationship ◽  
Personalized Recommendation ◽  
Current Interest ◽  
Quality Of Information ◽  
Rule Set ◽  
Information Recommendation ◽  
Browsing Behavior

To personalize the recommended learning information according to the interests of the learner, a recommendation rule set generation algorithm based on learner browsing interests was proposed. First, the learner's browsing behavior was captured. A multivariate regression method was used to calculate the quantitative relationship between the learner's browsing behavior and the degree of interest in the web page to generate a learner's current interest view (CIV). With this current interest view, a content-based collaborative filtering personalized information recommendation service was provided to learners. Then, a new weighted association rule algorithm was used to discover the associations between the items, so that the degree of recommendation was obtained. Furthermore, the degree of recommendation was used as a personalized recommendation service for learners with long-term interests. The results showed that the proposed algorithm effectively improved the quality of information recommendation and the real-time performance of the recommendation. Therefore, this algorithm has a good application value in the field of personalized learning recommendation.

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