Application of Improved DBSCAN Clustering Algorithm on Industrial Fault Text Data

The industrial fault text data are the special type of short texts, and they come from the records of faults in the factory. Clustering the industrial fault text data can reduce the redundant data and find out the hidden information, which is of great significance to improve the utilization of the i...

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Vydáno v:IEEE International Conference on Industrial Informatics (INDIN) Ročník 1; s. 461 - 468
Hlavní autoři: Wang, Xiaohan, Zhang, Lin, Zhang, Xuesong, Xie, Kunyu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 20.07.2020
Témata:
Clustering algorithm
Clustering algorithms
Conferences
DBSCAN
Degradation
Faces
Industrial fault text data
Informatics
Production facilities
Text clustering
ISSN:2378-363X
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Abstract The industrial fault text data are the special type of short texts, and they come from the records of faults in the factory. Clustering the industrial fault text data can reduce the redundant data and find out the hidden information, which is of great significance to improve the utilization of the industrial fault text data. The industrial fault text data are unstructured and irregular, so the clustering faces quite a few challenges. This paper introduces some existing algorithms for the clustering of short texts, and the shortcomings of them are briefly analyzed. This paper indicates that the main problem of the clustering of the industrial fault text data is the contradiction between the requirements and the setup of parameters, and it leads to low accuracy when cluster the corpus of different sizes. To increase the accuracy of clustering, an improved clustering algorithm is proposed which can solve this contradiction. The results of the comparative experiments show that the improved clustering algorithm has better performance than DBSCAN in corpus of different sizes on the industrial fault text data.
AbstractList The industrial fault text data are the special type of short texts, and they come from the records of faults in the factory. Clustering the industrial fault text data can reduce the redundant data and find out the hidden information, which is of great significance to improve the utilization of the industrial fault text data. The industrial fault text data are unstructured and irregular, so the clustering faces quite a few challenges. This paper introduces some existing algorithms for the clustering of short texts, and the shortcomings of them are briefly analyzed. This paper indicates that the main problem of the clustering of the industrial fault text data is the contradiction between the requirements and the setup of parameters, and it leads to low accuracy when cluster the corpus of different sizes. To increase the accuracy of clustering, an improved clustering algorithm is proposed which can solve this contradiction. The results of the comparative experiments show that the improved clustering algorithm has better performance than DBSCAN in corpus of different sizes on the industrial fault text data.
Author Zhang, Xuesong
Wang, Xiaohan
Xie, Kunyu
Zhang, Lin
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  email: 1285589283@qq.com
  organization: School of Automation Science and Electrical Engineering, Beihang University,Beijing,China
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Snippet The industrial fault text data are the special type of short texts, and they come from the records of faults in the factory. Clustering the industrial fault...
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StartPage 461
SubjectTerms Clustering algorithm
Clustering algorithms
Conferences
DBSCAN
Degradation
Faces
Industrial fault text data
Informatics
Production facilities
Text clustering
Title Application of Improved DBSCAN Clustering Algorithm on Industrial Fault Text Data
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