A Data-Driven Clustering Approach for Fault Diagnosis. Hou, J. & Xiao, B. IEEE Access, 5:26512–26520, 2017. Conference Name: IEEE Accessdoi abstract bibtex Clustering is an important approach in fault diagnosis. The dominant sets algorithm is a graph-based clustering algorithm, which defines the dominant set as a concept of a cluster. In this paper, we make an in-depth investigation of the dominant sets algorithm. As a result, we find that this algorithm is dependent on the similarity parameter in constructing the pairwise similarity matrix, and has the tendency to generate spherical clusters only. Based on the merits and drawbacks of this algorithm, we apply the histogram equalization transformation to the similarity matrices for the purpose of removing the influence of similarity parameters, and then use a density-based cluster expansion process to improve the clustering results. In experimental validation of the proposed algorithm, we use two criterions to evaluate the clustering results in order to arrive at convincing conclusions. Data clustering experiments on ten data sets and fault detection experiments on the Tennessee Eastman process demonstrate the effectiveness of the proposed algorithm.
@article{hou_data-driven_2017,
title = {A {Data}-{Driven} {Clustering} {Approach} for {Fault} {Diagnosis}},
volume = {5},
issn = {2169-3536},
doi = {10.1109/ACCESS.2017.2771365},
abstract = {Clustering is an important approach in fault diagnosis. The dominant sets algorithm is a graph-based clustering algorithm, which defines the dominant set as a concept of a cluster. In this paper, we make an in-depth investigation of the dominant sets algorithm. As a result, we find that this algorithm is dependent on the similarity parameter in constructing the pairwise similarity matrix, and has the tendency to generate spherical clusters only. Based on the merits and drawbacks of this algorithm, we apply the histogram equalization transformation to the similarity matrices for the purpose of removing the influence of similarity parameters, and then use a density-based cluster expansion process to improve the clustering results. In experimental validation of the proposed algorithm, we use two criterions to evaluate the clustering results in order to arrive at convincing conclusions. Data clustering experiments on ten data sets and fault detection experiments on the Tennessee Eastman process demonstrate the effectiveness of the proposed algorithm.},
journal = {IEEE Access},
author = {Hou, Jian and Xiao, Bing},
year = {2017},
note = {Conference Name: IEEE Access},
keywords = {Algorithm design and analysis, Clustering, Clustering algorithms, Data mining, Fault diagnosis, Partitioning algorithms, Shape, cluster expansion, dominant set, fault diagnosis},
pages = {26512--26520},
}
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