A robust unsupervised consensus control chart pattern recognition framework. Issue 19 (1st November 2015)

• Record Type: Journal Article
• Authors: Haghtalab, Siavash; Xanthopoulos, Petros; Madani, Kaveh
• Is Part Of: Expert systems with applications. Volume 42:Issue 19(2015)
• Page Start: 6767
• Page End: 6776
• Publication Date: 2015-11-01
• Subjects: Consensus clustering -- Unsupervised learning -- Control chart pattern recognition -- k-Means -- Spectral clustering -- Graph partitioning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.04.069
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We propose an unsupervised consensus approach for the control chart pattern recognition problem. We provide computational evidence of consensus clustering robustness. We provide motivation for further research on unsupervised learning for the CCPR problem. 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.