Positive and Negative Association Rules Mining for Mental Health Analysis of College Students

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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A Novel Approach on Negative Association Rules

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch220 ◽

2011 ◽

pp. 1425-1430

Author(s):

Ioannis N. Kouris

Keyword(s):

Association Rules ◽

Positive Association ◽

Negative Influence ◽

Negative Association ◽

Association Rules Mining ◽

Novel Approach ◽

Negative Effect ◽

Negative Association Rules ◽

The Subject ◽

Definition Of

Research in association rules mining has initially concentrated in solving the obvious problem of finding positive association rules; that is rules among items that exist in the stored transactions. It was only several years after that the possibility of finding also negative association rules became especially appealing and was investigated. Nevertheless researchers based their assumptions regarding negative association rules on the absence of items from transactions. This assumption though besides being dubious, since it equated the absence of an item with a conflict or negative effect on the rest items, it also brought out a series of computational problems with the amount of possible patterns that had to be examined and analyzed. In this work we give an overview of the works having engaged with the subject until now and present a novel view for the definition of negative influence among items.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3435 ◽

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.

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Study on Multidimensional Negative Association Rules

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.1721 ◽

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.

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