scholarly journals Mining Fuzzy Association Rules from Web Usage Quantitative Data

2016 ◽  
Author(s):  
Ujwala Manoj Patil ◽  
Patil J.B
Keyword(s):  
Association Rules ◽  
Quantitative Data ◽  
Fuzzy Association Rules ◽  
Web Usage

scholarly journals Web usage mining with evolutionary extraction of temporal fuzzy association rules

2013 ◽  
Vol 54 ◽  
pp. 66-72 ◽  
Author(s):  
Stephen G. Matthews ◽  
Mario A. Gongora ◽  
Adrian A. Hopgood ◽  
Samad Ahmadi
Keyword(s):  
Association Rules ◽  
Web Usage Mining ◽  
Fuzzy Association Rules ◽  
Web Usage

A Survey on Fuzzy Association Rule Mining

2013 ◽  
Vol 9 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Harihar Kalia ◽  
Satchidananda Dehuri ◽  
Ashish Ghosh
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Quantitative Data ◽  
Association Rule Mining ◽  
Real Life ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Fuzzy Association Rule ◽  
Mining Algorithms ◽  
Fuzzy Association Rule Mining

Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.

Mining Fuzzy Association Rules on Has-A and Is-A Hierarchical Structures

2007 ◽  
Vol 11 (4) ◽  
pp. 423-432 ◽  
Author(s):  
Been-Chian Chien ◽  
◽  
Ming-Huang Zhong ◽  
Jeng-Jung Wang ◽  
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Quantitative Data ◽  
Hierarchical Structures ◽  
Fuzzy Association Rules ◽  
Hierarchical Relationships ◽  
Item Data

Preliminary studies on data mining focus on finding association rules from transaction databases containing items without relationships among them. However, relationships among items often exist in real applications. Most of the previous works only concern about Is-A hierarchy. In this paper, hierarchical relationships include a Has-A hierarchy and multiple Is-A hierarchies are discussed. The proposed method first reduces a Has-A & Is-A hierarchy into an extended Has-A hierarchy using the IsA-Reduce algorithm. The quantitative data is transformed into fuzzy items. The RPFApriori algorithm is then applied to find fuzzy association rules from the fuzzy item data and the extended Has-A hierarchy.

Parallel mining and application of fuzzy association rules

2006 ◽  
Vol 1 (2) ◽  
pp. 177-182
Author(s):  
Jian-jiang Lu ◽  
Bao-wen Xu ◽  
Xiao-feng Zou ◽  
Da-zhou Kang ◽  
Yan-hui Li ◽  
...  
Keyword(s):  
Association Rules ◽  
Fuzzy Association Rules ◽  
Parallel Mining

scholarly journals Mining Fuzzy Association Rules in the Framework of AFS Theory

2015 ◽  
Vol 2 (3) ◽  
pp. 261-270 ◽  
Author(s):  
Bo Wang ◽  
Xiao-dong Liu ◽  
Li-dong Wang
Keyword(s):  
Association Rules ◽  
Fuzzy Association Rules ◽  
Afs Theory
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