Evaluation of agility in supply chains using fuzzy association rules mining

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

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Supplier selection using fuzzy association rules mining approach

International Journal of Production Research ◽

10.1080/00207540600665836 ◽

2007 ◽

Vol 45 (6) ◽

pp. 1323-1353 ◽

Cited By ~ 39

Author(s):

V. Jain ◽

S. Wadhwa ◽

S. G. Deshmukh

Keyword(s):

Association Rules ◽

Supplier Selection ◽

Association Rules Mining ◽

Fuzzy Association Rules

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A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan

Mathematics ◽

10.3390/math8111908 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1908

Author(s):

Shianghau Wu

Keyword(s):

Association Rules ◽

Mainland China ◽

Sharing Economy ◽

Association Rules Mining ◽

The Sustainable Development ◽

Research Results ◽

Fuzzy Association Rules ◽

Low Energy Consumption ◽

The World ◽

Bike Sharing

The sharing economy has become an important issue in recent years. Many researchers have paid attention to its application around the world. The sharing of bikes, as one of the major applications of the sharing economy, has shown its advantage in the realm of environmental protection and low energy consumption. However, bike-sharing system has encountered problems in certain regions. This arouses the concern about the sustainable development of the bike-sharing system. This research focused on the failure case of oBike in Taiwan. This research used text mining and fuzzy association rules mining methods to evaluate Taiwan’s public opinion about the oBike in order to verify the reasons for oBike’s failure in Taiwan. This study also made a comparison between the bike-sharing system in Mainland China and Taiwan. The research results explored the factors of oBike’s failure in Taiwan and showcased the problems of bike-sharing systems in different regions. The research results also offer useful information for bike-sharing companies and the authorities concerned in order to develop a sustainable bike-sharing system.

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An Efficient Weighted Association Rules Mining Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.1247 ◽

2013 ◽

Vol 333-335 ◽

pp. 1247-1250 ◽

Cited By ~ 1

Author(s):

Na Xin Peng

Keyword(s):

Association Rules ◽

Frequent Itemsets ◽

Experimental Results ◽

Apriori Algorithm ◽

Association Rules Mining ◽

Fuzzy Association Rules ◽

Weighted Association Rules ◽

Mining Algorithm ◽

Pruning Strategy

Aiming at the problem that most of weighted association rules algorithm have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, Boolean weighted association rules algorithm and weighted fuzzy association rules algorithm are presented, which use pruning strategy of Apriori algorithm so as to improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.

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