Evaluation of sustainability using fuzzy association rules mining

2011 ◽  
Vol 13 (6) ◽  
pp. 809-819 ◽  
Author(s):  
S. Vinodh ◽  
K. Eazhil Selvan ◽  
N. Hari Prakash
Keyword(s):  
Association Rules ◽  
Association Rules Mining ◽  
Fuzzy Association Rules

Extracting relations of crime rates through fuzzy association rules mining

2019 ◽  
Vol 50 (2) ◽  
pp. 448-467 ◽  
Author(s):  
Zhongjie Zhang ◽  
Jian Huang ◽  
Jianguo Hao ◽  
Jianxing Gong ◽  
Hao Chen
Keyword(s):  
Association Rules ◽  
Crime Rates ◽  
Association Rules Mining ◽  
Fuzzy Association Rules

Applying cluster-based fuzzy association rules mining framework into EC environment

2012 ◽  
Vol 12 (8) ◽  
pp. 2114-2122 ◽  
Author(s):  
Hung-Pin Chiu ◽  
Yi-Tsung Tang ◽  
Kun-Lin Hsieh
Keyword(s):  
Association Rules ◽  
Association Rules Mining ◽  
Fuzzy Association Rules

Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 842-845 ◽  
Author(s):  
Jia Mei Guo ◽  
Yin Xiang Pei
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Clustering Algorithm ◽  
Original Data ◽  
Data Set ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Redundant Data ◽  
Fuzzy Partitions ◽  
Rules Extraction

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.

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

Mathematics ◽  
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.

An Efficient Weighted Association Rules Mining Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1247-1250 ◽  
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.

Different Types of Association Rules Mining Review

2012 ◽  
Vol 241-244 ◽  
pp. 1589-1592
Author(s):  
Jun Tan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Weighted Association Rules ◽  
Application Systems ◽  
Different Types ◽  
Multi Level

In recent years, many application systems have generate large quantities of data, so it is no longer practical to rely on traditional database technique to analyze these data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining technology. The paper first presents the basic concept of association rule mining, then discuss a few different types of association rules mining including multi-level association rules, multidimensional association rules, weighted association rules, multi-relational association rules, fuzzy association rules.

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