Proceedings ArticleDOI
Research of Mining Effective and Weighted Association Rules Based on Dual Confidence
Yihua Zhong,Yuxin Liao +1 more
- pp 1228-1231
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TLDR
A new algorithm which can mine effective weighted rules is proposed in this paper, which is on the basis of the dual confidence association rules used in algorithm.Abstract:
Association rule is an important model in data mining. However, traditional association rules are mostly based on the support and confidence metrics, and most algorithms and researches assumed that each attribute in the database is equal. In fact, because the user preference to the item is different, the mining rules using the existing algorithms are not always appropriate to users. By introducing the concept of weighted dual confidence, a new algorithm which can mine effective weighted rules is proposed in this paper, which is on the basis of the dual confidence association rules used in algorithm. The case studies show that the algorithm can reduce the large number of meaningless association rules and mine interesting negative association rules in real life.read more
Citations
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Proceedings ArticleDOI
A comprehensive survey of association rules on quantitative data in data mining
Anjana Gosain,Maneela Bhugra +1 more
TL;DR: This paper presents different algorithms given by various researches to generate association rules among quantitative data and does comparative analysis of different algorithms for association rules based on various parameters.
Proceedings ArticleDOI
Discovering association rules partially devoid of dissociation by weighted confidence
Subrata Datta,Subrata Bose +1 more
TL;DR: It is shown that the weighted support, weighted confidence approach increases the chance of discovering rules which are less dissociated compared to the traditional support-confidence framework provided the authors maintain same level of minsupp and minconf in both cases.
Journal ArticleDOI
Association Rule Mining Method Based on the Similarity Metric of Tuple-Relation in Indoor Environment
TL;DR: A new R-FP-growth (tuple-relation frequent pattern growth) algorithm for mining association rules in indoor environment is proposed, which makes comprehensive use of the co-occurrence probability, conditional probability, and multiple potential association information among POI sets to form a new support-confidence-relation constraint framework and to improve the quality and application value of mining results.
Journal ArticleDOI
Frequent Itemset Mining for Big Data Using Greatest Common Divisor Technique
TL;DR: The proposed algorithm introduces a new method to parallelize the frequent itemset mining without the need to generate candidate itemsets and also it avoids any communication overhead between the participated nodes.
Proceedings ArticleDOI
EO-ARM: An efficient and optimized k-map based positive-negative association rule mining technique
Chandrasekar Ravi,Neelu Khare +1 more
TL;DR: EO-ARM, an Efficient and Optimized Positive-Negative Association Rule Mining algorithm, which produces both positive as well as negative association rules and optimizes the association rules by introducing a contingency matrix based correlation measure which prunes less interesting rules thereby overcoming the existing limitations.
References
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Proceedings ArticleDOI
An Effective Algorithm for Mining Positive and Negative Association Rules
Honglei Zhu,Zhigang Xu +1 more
TL;DR: An efficient algorithm (PNAR) for mining both positive and negative association rules in databases with a correlation coefficient measure and pruning strategies is presented.
Proceedings ArticleDOI
Research on Mining Positive and Negative Association Rules Based on Dual Confidence
TL;DR: The experimental result shows that positive and negative association rules mining algorithm can reduce the scale of meaningless association rules, and mine a lot of interestingnegative association rules.
Proceedings ArticleDOI
Mining Positive and Negative Weighted Association Rules from Frequent Itemsets Based on Interest
TL;DR: A new model in the paper of extending the support-confidence framework with a sliding interest measure could avoid generating misleading rules by discovering interesting weighted negative association rules from large database and deleting the contrary rules.
Proceedings ArticleDOI
Mining association rules with new measure criteria
TL;DR: The experimental results demonstrate that the Chi-Squared test is effective on reducing the amount of patterns via merging support and cover constrain, and the efficiency and veracity of mining association rules are improved.
Journal Article
Improved Weighted Association Rules Mining Method
TL;DR: A new method is presented that can hold the downward closed property by using an improved model of weighted support measurements when mining association rules, and it proves that the method can quickly and efficiently mine important association rules.
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