Feature selection based on fuzzy clustering analysis and association rule mining for soft-sensor

2014 ◽  
Author(s):  
Ling Wang ◽  
Hui Guo
Keyword(s):  
Feature Selection ◽  
Fuzzy Clustering ◽  
Association Rule ◽  
Clustering Analysis ◽  
Association Rule Mining ◽  
Soft Sensor ◽  
Rule Mining ◽  
Fuzzy Clustering Analysis

Reducing the Feature Space Using Constraint-Governed Association Rule Mining

2017 ◽  
Vol 26 (1) ◽  
pp. 139-152
Author(s):  
◽  
M. Umme Salma
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Association Rule ◽  
Association Rule Mining ◽  
Medical Data ◽  
Cancer Surveillance ◽  
Feature Subset ◽  
Rule Mining ◽  
Data Set

AbstractRecent advancements in science and technology and advances in the medical field have paved the way for the accumulation of huge amount of medical data in the digital repositories, where they are stored for future endeavors. Mining medical data is the most challenging task as the data are subjected to many social concerns and ethical issues. Moreover, medical data are more illegible as they contain many missing and misleading values and may sometimes be faulty. Thus, pre-processing tasks in medical data mining are of great importance, and the main focus is on feature selection, because the quality of the input determines the quality of the resultant data mining process. This paper provides insight to develop a feature selection process, where a data set subjected to constraint-governed association rule mining and interestingness measures results in a small feature subset capable of producing better classification results. From the results of the experimental study, the feature subset was reduced to more than 50% by applying syntax-governed constraints and dimensionality-governed constraints, and this resulted in a high-quality result. This approach yielded about 98% of classification accuracy for the Breast Cancer Surveillance Consortium (BCSC) data set.

scholarly journals Association Rule-Based Feature Mining for Automated Fault Diagnosis of Rolling Bearing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yuan Li ◽  
Jinjiang Wang ◽  
Lixiang Duan ◽  
Tangbo Bai ◽  
Xuduo Wang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Matrix ◽  
Fault Classification ◽  
Rule Mining ◽  
Bearing Fault ◽  
New Association

Effective and efficient diagnosis methods are highly demanded to improve system reliability. Comparing with conventional fault diagnosis methods taking a forward approach (e.g., feature extraction, feature selection, and fusion, and then fault diagnosis), this paper presents a new association rule mining method which provides an inverse approach unearthing the underlying relation between labeled defects and extracted features for bearing fault analysis. Instead of evenly dividing methods used in traditional association rule mining, a new association rule mining approach based on the equal probability discretization method is presented in this study. First, a series of extracted features of signal data are discretized following the guideline of equalized probability distribution of the data in order to avoid excessive concentration or decentralized data. Next, the data matrix composed of arrays of discretized features and defect labels is exploited to generate the association rules representing the relation between the features and fault types. Experimental study on a bearing test reveals that the proposed method can generate a series of underlying association rules for bearing fault diagnosis, and the related features selected by the proposed method can be used directly to analyze bearing signals for fault classification and defect severity identification. As a new feature selection method, it possesses prominent superiority compared to traditional PCA, KPCA, and LLE dimension reduction methods.

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