Association Rule Mining Algorithm Based On Fuzzy Association Rules Lattice and Apriori

2013 ◽  
Vol 8 (8) ◽  
pp. 399-406
Author(s):  
Jiebing Liu ◽  
Baoxiang Liu ◽  
Jianming Liu ◽  
Huanhuan Chen
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Mining Algorithm

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.

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.

An Efficient Communication Network SDH Alarm Association Rule Mining Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 1870-1873
Author(s):  
Hui Sheng Gao ◽  
Ying Min Li
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Time Window ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Event Window ◽  
Mining Algorithm ◽  
Efficient Communication ◽  
Algorithm Framework

WINEPI algorithm is kind of data mining technology that is widely used in alarm association rules mining. Based on the classic WINEPI algorithm, we apply event window instead of time window to improve the exploration result, meanwhile we use FP-Growth algorithm framework instead of Apriori algorithm framework , thus improving efficiency. Based on the alarm time attribute we find interesting alarm association rules further. Experiments show that compared with the classic WINEPI algorithm our improved approach have advantages in reducing the mining error rate and gaining more interesting alarm association rules.

Implement of Hyper-Graph System Based on Three-Dimensional Matrix Model

2014 ◽  
Vol 644-650 ◽  
pp. 1809-1812
Author(s):  
Hua Jian Lan ◽  
Yuan Xin Tang ◽  
Xin Rui Song ◽  
Guang Lu Yu
Keyword(s):  
Big Data ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Three Dimensional ◽  
Processing Capacity ◽  
Rule Mining ◽  
Dimensional Matrix ◽  
Mining Algorithm ◽  
Graph System

At present the majority of association rule mining algorithm only uses support and confidence to evaluate association rules, association rules which may contain a large number of redundant, meaningless, Introducing the concepts of hyper-graph and system and exploring to construct the hyper-graph on the model of three-dimensional matrix. According to the characteristics of Big Data, the new hyper-edge definition method is adopted combining the concept of system, thus improving the processing capacity.

scholarly journals Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zhicong Kou ◽  
Lifeng Xi
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Binary Particle Swarm Optimization ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Product Features

An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

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