Data mining, machine learning, unsupervised learing, control chart pattern recognition, clustering, consensus clustering, ensemble methods
Early identification and detection of abnormal time series patterns is vital for a number of manufacturing. Slide shifts and alterations of time series patterns might be indicative of some anomaly in the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less 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 for practical purposes. In this study we propose the use of a consensus clustering framework. Computational results show robust behavior compared to individual clustering algorithms.
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Master of Science (M.S.)
College of Engineering and Computer Science
Industrial Engineering and Management Systems
Industrial Engineering; Systems Engineering
Length of Campus-only Access
Masters Thesis (Open Access)
Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic
Haghtalab, Siavash, "An Unsupervised Consensus Control Chart Pattern Recognition Framework" (2014). Electronic Theses and Dissertations, 2004-2019. 4505.