Extended Negative Association Rules and the Corresponding Mining Algorithm

2006 ◽  
pp. 159-168 ◽  
Author(s):  
Min Gan ◽  
Mingyi Zhang ◽  
Shenwen Wang
Keyword(s):  
Association Rules ◽  
Negative Association ◽  
Mining Algorithm ◽  
Negative Association Rules

A Study of Negative Association Rules Mining Algorithm Based on Multi-Database

2013 ◽  
Vol 756-759 ◽  
pp. 3435-3439
Author(s):  
Xu Shan Peng ◽  
Ping Cheng ◽  
Mo Gei Wang
Keyword(s):  
Decision Making ◽  
Association Rules ◽  
Mutual Exclusion ◽  
Negative Association ◽  
Database Mining ◽  
Technology Research ◽  
Association Rules Mining ◽  
Mining Algorithm ◽  
Database Technology ◽  
Negative Association Rules

the mutual exclusion relationships among data items are reflected by negative association rules, whitch is very important on the decision-making analysis. In the last several years, negative association rules are frequently researched, while the study object of it is single mining of database now. With the development of database technology, multi-database mining is more and more important. On the basis of analyzing the related technology, research status and shortage of present negative association rules mining , the selecting rules, weighted synthesis and algorithm are discussed on multi-datobase.

Study on Multidimensional Negative Association Rules

2014 ◽  
Vol 644-650 ◽  
pp. 1721-1724
Author(s):  
He Jiang ◽  
Ai Xin Yang ◽  
Hong Jun Yu
Keyword(s):  
Association Rules ◽  
Algorithm Design ◽  
Negative Association ◽  
Technology Research ◽  
Design Problems ◽  
Mining Technology ◽  
Association Rules Mining ◽  
Mining Algorithm ◽  
Negative Association Rules ◽  
Transaction Database

With the deepening of the negative association rules mining technology research, many key problems have been solved, but the solution of these problems are all on a single predicate in the transaction database. However, the data in the database often involves multiple predicates. This paper focuses on solving multi-dimensional support and confidence, negative association rules mining algorithm design problems. The experiment proves that the algorithm is correct and efficiency.

scholarly journals A novel interestingness measure based on fusion model for association rules mining

2021 ◽  
Vol 336 ◽  
pp. 05009
Author(s):  
Junrui Yang ◽  
Lin Xu
Keyword(s):  
Association Rules ◽  
Negative Association ◽  
Fusion Model ◽  
Interestingness Measures ◽  
Association Rules Mining ◽  
Interestingness Measure ◽  
Advantages And Disadvantages ◽  
Measure Model ◽  
Negative Association Rules ◽  
The Difference

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.

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