scholarly journals Fuzzy association rules mining method for design knowledge of mechanical structure symmetry

2016 ◽  
Author(s):  
Juncheng Yang ◽  
Zhiyong Ma
Keyword(s):  
Association Rules ◽  
Design Knowledge ◽  
Mining Method ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Mechanical Structure

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

Mining Interesting Association Rules of Students Suffering Study Anxieties Using SLP-Growth Algorithm

2012 ◽  
Vol 3 (2) ◽  
pp. 24-41 ◽  
Author(s):  
Tutut Herawan ◽  
Prima Vitasari ◽  
Zailani Abdullah
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Teaching Strategies ◽  
Engineering Students ◽  
Mining Method ◽  
Association Rules Mining ◽  
Quality Of Learning ◽  
Pattern Growth

One of the most popular techniques used in data mining applications is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, called SLP-Growth (Significant Least Pattern Growth) for capturing interesting rules from students suffering mathematics and examination anxieties datasets. The datasets were taken from a survey exploring study anxieties among engineering students in Universiti Malaysia Pahang (UMP). The results of this research provide useful information for educators to make decisions on their students more accurately and adapt their teaching strategies accordingly. It also can assist students in handling their fear of mathematics and examination and increase the quality of learning.

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.

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