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 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..

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>

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.

Research of mining meta-association rules for dynamic association rule based on model of Grey-Markov

2009 ◽  
Vol 28 (9) ◽  
pp. 2353-2356 ◽  
Author(s):  
Jun LIU ◽  
Yan-feng XIE ◽  
Zhong-lin ZHANG ◽  
Li-min JIA
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Rule Based ◽  
Dynamic Association

scholarly journals Coverage-Based Classification Using Association Rule Mining

2020 ◽  
Vol 10 (20) ◽  
pp. 7013
Author(s):  
Jamolbek Mattiev ◽  
Branko Kavsek
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Classification Accuracy ◽  
Real Life ◽  
Classification Model ◽  
Classification Algorithms ◽  
Classical Class ◽  
Average Accuracy ◽  
Relevance Measures ◽  
Class Association Rule

Building accurate and compact classifiers in real-world applications is one of the crucial tasks in data mining nowadays. In this paper, we propose a new method that can reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose a new associative classifier that selects “strong” class association rules based on overall coverage of the learning set. The advantage of the proposed classifier is that it generates significantly smaller rules on bigger datasets compared to traditional classifiers while maintaining the classification accuracy. We also discuss how the overall coverage of such classifiers affects their classification accuracy. Performed experiments measuring classification accuracy, number of classification rules and other relevance measures such as precision, recall and f-measure on 12 real-life datasets from the UCI ML repository (Dua, D.; Graff, C. UCI Machine Learning Repository. Irvine, CA: University of California, 2019) show that our method was comparable to 8 other well-known rule-based classification algorithms. It achieved the second-highest average accuracy (84.9%) and the best result in terms of average number of rules among all classification methods. Although not achieving the best results in terms of classification accuracy, our method proved to be producing compact and understandable classifiers by exhaustively searching the entire example space.

scholarly journals Generating a Condensed Representation for Positive and Negative Association Rules

2021 ◽  
pp. 175-186
Author(s):  
Bemarisika Parfait ◽  
André Totohasina
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Negative Association ◽  
Condensed Representation ◽  
Rule Set ◽  
Closed Itemsets ◽  
Negative Association Rules ◽  
Maximal Frequent Itemsets ◽  
Highly Correlated ◽  
Common Association

Given a large collection of transactions containing items, a basic common association rules problem is the huge size of the extracted rule set. Pruning uninteresting and redundant association rules is a promising approach to solve this problem. In this paper, we propose a Condensed Representation for Positive and Negative Association Rules representing non-redundant rules for both exact and approximate association rules based on the sets of frequent generator itemsets, frequent closed itemsets, maximal frequent itemsets, and minimal infrequent itemsets in database B. Experiments on dense (highly-correlated) databases show a significant reduction of the size of extracted association rule set in database B.

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
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