scholarly journals Association Rule Mining Method Based on the Similarity Metric of Tuple-Relation in Indoor Environment

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 52041-52051 ◽  
Author(s):  
Naixia Mou ◽  
Hongen Wang ◽  
Hengcai Zhang ◽  
Xin Fu
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Indoor Environment ◽  
Mining Method ◽  
Rule Mining ◽  
Similarity Metric

scholarly journals Machine Learning Based Quantitative Association Rule Mining Method for Evaluating Cellular Network Performance

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166815-166822
Author(s):  
Guanghui Fan ◽  
Wenjuan Shi ◽  
Liang Guo ◽  
Jun Zeng ◽  
Kaixuan Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Association Rule ◽  
Association Rule Mining ◽  
Cellular Network ◽  
Network Performance ◽  
Mining Method ◽  
Rule Mining ◽  
Quantitative Association Rule

Association rule mining using chaotic gravitational search algorithm for discovering relations between manufacturing system capabilities and product features

2019 ◽  
Vol 27 (3) ◽  
pp. 213-232 ◽  
Author(s):  
Zhicong Kou
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Manufacturing System ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
New Product ◽  
Mining Method ◽  
Rule Mining ◽  
Product Features ◽  
Gravitational Search

An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for efficient and cost-effective product development and production. This article proposes a chaotic gravitational search algorithm–based association rule mining method for discovering the hidden relationship between manufacturing system capabilities and product features. The extracted rules would be utilized to predict capability requirements of various machines for the new product with different features. We use two strategies to incorporate chaos into gravitational search algorithm: one strategy is to embed chaotic map functions into the gravitational constant of gravitational search algorithm; the other is to use sequences generated by chaotic maps to substitute random numbers for different parameters of gravitational search algorithm. In order to improve the applicability of chaotic gravitational search algorithm–based association rule mining, a novel overlapping measure indication is further proposed to eliminate those unuseful rules. The proposed method is relatively simple and easy to implement. The rules generated by chaotic gravitational search algorithm–based association rule mining are accurate, interesting, and comprehensible to the user. The performance comparison indicates that chaotic gravitational search algorithm–based association rule mining outperforms other regular methods (e.g. Apriori) for association rule mining. The experimental results illustrate that chaotic gravitational search algorithm–based association rule mining is capable of discovering important association rules between manufacturing system capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

Stellar spectra association rule mining method based on the weighted frequent pattern tree

2013 ◽  
Vol 13 (3) ◽  
pp. 334-342 ◽  
Author(s):  
Jiang-Hui Cai ◽  
Xu-Jun Zhao ◽  
Shi-Wei Sun ◽  
Ji-Fu Zhang ◽  
Hai-Feng Yang
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Pattern ◽  
Mining Method ◽  
Rule Mining ◽  
Stellar Spectra ◽  
Frequent Pattern Tree
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