A Robust Unsupervised Consensus Control Chart Pattern Recognition Framework
Keywords
Consensus clustering; Control chart pattern recognition; Graph partitioning; k-Means; Spectral clustering; Unsupervised learning
Abstract
Early identification and detection of abnormal patterns is vital for a number of applications. In manufacturing for example, slide shifts and alterations of patterns might be indicative of some production process anomaly, such as machinery malfunction. Usually due to the continuous flow of data, monitoring of manufacturing processes and other types of applications requires automated control chart pattern recognition (CCPR) algorithms. Most of the CCPR literature consists of supervised classification algorithms. Fewer studies consider unsupervised versions of the problem. Despite the profound advantage of unsupervised methodology for less manual data labeling their use is limited due to the fact that their performance is not robust enough and might vary significantly from one algorithm to another. In this paper, we propose the use of a consensus clustering framework that takes care of this shortcoming and produces results that are robust with respect to the chosen pool of algorithms. Computational results show that the proposed method achieves not less than 79.10% G-mean with most of test instances achieving higher than 90%. This happens even when in the algorithmic pool are included algorithms with performance less than 15%. To our knowledge, this is the first paper proposing an unsupervised consensus learning approach in CCPR. The proposed approach is promising and provides a new research direction in unsupervised CCPR literature.
Publication Date
5-30-2015
Publication Title
Expert Systems with Applications
Volume
42
Issue
19
Number of Pages
6767-6776
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1016/j.eswa.2015.04.069
Copyright Status
Unknown
Socpus ID
84930045920 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84930045920
STARS Citation
Haghtalab, Siavash; Xanthopoulos, Petros; and Madani, Kaveh, "A Robust Unsupervised Consensus Control Chart Pattern Recognition Framework" (2015). Scopus Export 2015-2019. 1096.
https://stars.library.ucf.edu/scopus2015/1096