Parallel mining of fuzzy association rules on dense data sets

2014 ◽  
Author(s):  
Michal Burda ◽  
Viktor Pavliska ◽  
Radek Valasek
Keyword(s):  
Association Rules ◽  
Data Sets ◽  
Fuzzy Association Rules ◽  
Parallel Mining ◽  
Dense Data

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

Effective Mining of Weighted Fuzzy Association Rules

2010 ◽  
pp. 47-64 ◽  
Author(s):  
Maybin Muyeba ◽  
M. Sulaiman Khan ◽  
Frans Coenen
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Real Data ◽  
Closure Property ◽  
Data Sets ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Novel Approach ◽  
Downward Closure

A novel approach is presented for effectively mining weighted fuzzy association rules (ARs). The authors address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance wrt some user defined criteria. Most works on weighted association rule mining do not address the downward closure property while some make assumptions to validate the property. This chapter generalizes the weighted association rule mining problem with binary and fuzzy attributes with weighted settings. Their methodology follows an Apriori approach but employs T-tree data structure to improve efficiency of counting itemsets. The authors’ approach avoids pre and post processing as opposed to most weighted association rule mining algorithms, thus eliminating the extra steps during rules generation. The chapter presents experimental results on both synthetic and real-data sets and a discussion on evaluating the proposed approach.

scholarly journals Parallel mining for fuzzy association rules.

2012 ◽  
Vol 20 (2) ◽  
Author(s):  
Phan Xuan Hieu ◽  
Hà Quang Thụy
Keyword(s):  
Association Rules ◽  
Fuzzy Association Rules ◽  
Parallel Mining

A Distributed Algorithm for Mining Fuzzy Association Rules in Traditional Databases

2011 ◽  
pp. 685-705
Author(s):  
Wai-Ho Au
Keyword(s):  
Distributed System ◽  
Association Rules ◽  
Distributed Algorithm ◽  
Data Sets ◽  
Linguistic Variables ◽  
Linguistic Terms ◽  
Fuzzy Association Rules ◽  
Interestingness Measure ◽  
The Difference ◽  
Fuzzy Association Rule

The mining of fuzzy association rules has been proposed in the literature recently. Many of the ensuing algorithms are developed to make use of only a single processor or machine. They can be further enhanced by taking advantage of the scalability of parallel or distributed computer systems. The increasing ability to collect data and the resulting huge data volume make the exploitation of parallel or distributed systems become more and more important to the success of fuzzy association rule mining algorithms. This chapter proposes a new distributed algorithm, called DFARM, for mining fuzzy association rules from very large databases. Unlike many existing algorithms that adopt the support-confidence framework such that an association is considered interesting if it satisfies some user-specified minimum percentage thresholds, DFARM embraces an objective measure to distinguish interesting associations from uninteresting ones. This measure is defined as a function of the difference in the actual and the expected number of tuples characterized by different linguistic variables (attributes) and linguistic terms (attribute values). Given a database, DFARM first divides it into several horizontal partitions and assigns them to different sites in a distributed system. It then has each site scan its own database partition to obtain the number of tuples characterized by different linguistic variables and linguistic terms (i.e., the local counts), and exchange the local counts with all the other sites to find the global counts. Based on the global counts, the values of the interestingness measure are computed, and the sites can uncover interesting associations. By repeating this process of counting, exchanging counts, and calculating the interestingness measure, it unveils the underlying interesting associations hidden in the data. We implemented DFARM in a distributed system and used a popular benchmark data set to evaluate its performance. The results show that it has very good size-up, speedup, and scale-up performance. We also evaluated the effectiveness of the proposed interestingness measure on two synthetic data sets. The experimental results show that it is very effective in differentiating between interesting and uninteresting associations.

A Distributed Algorithm for Mining Fuzzy Association Rules in Traditional Databases

2009 ◽  
pp. 2427-2447
Author(s):  
Wai-Ho Au
Keyword(s):  
Distributed System ◽  
Association Rules ◽  
Distributed Algorithm ◽  
Data Sets ◽  
Linguistic Variables ◽  
Linguistic Terms ◽  
Fuzzy Association Rules ◽  
Interestingness Measure ◽  
The Difference ◽  
Fuzzy Association Rule

The mining of fuzzy association rules has been proposed in the literature recently. Many of the ensuing algorithms are developed to make use of only a single processor or machine. They can be further enhanced by taking advantage of the scalability of parallel or distributed computer systems. The increasing ability to collect data and the resulting huge data volume make the exploitation of parallel or distributed systems become more and more important to the success of fuzzy association rule mining algorithms. This chapter proposes a new distributed algorithm, called DFARM, for mining fuzzy association rules from very large databases. Unlike many existing algorithms that adopt the support-confidence framework such that an association is considered interesting if it satisfies some user-specified minimum percentage thresholds, DFARM embraces an objective measure to distinguish interesting associations from uninteresting ones. This measure is defined as a function of the difference in the actual and the expected number of tuples characterized by different linguistic variables (attributes) and linguistic terms (attribute values). Given a database, DFARM first divides it into several horizontal partitions and assigns them to different sites in a distributed system. It then has each site scan its own database partition to obtain the number of tuples characterized by different linguistic variables and linguistic terms (i.e., the local counts), and exchange the local counts with all the other sites to find the global counts. Based on the global counts, the values of the interestingness measure are computed, and the sites can uncover interesting associations. By repeating this process of counting, exchanging counts, and calculating the interestingness measure, it unveils the underlying interesting associations hidden in the data. We implemented DFARM in a distributed system and used a popular benchmark data set to evaluate its performance. The results show that it has very good size-up, speedup, and scale-up performance. We also evaluated the effectiveness of the proposed interestingness measure on two synthetic data sets. The experimental results show that it is very effective in differentiating between interesting and uninteresting associations.

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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