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An Unsupervised Consensus Control Chart Pattern Recognition Framework

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Date Issued:
2014
Abstract/Description:
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 anomalyin 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 advantageof unsupervised methodology for less manual data labeling their use is limited due to thefact 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.
Title: An Unsupervised Consensus Control Chart Pattern Recognition Framework.
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Name(s): Haghtalab, Siavash, Author
Xanthopoulos, Petros, Committee Chair
Pazour, Jennifer, Committee Member
Rabelo, Luis, Committee Member
, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2014
Publisher: University of Central Florida
Language(s): English
Abstract/Description: 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 anomalyin 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 advantageof unsupervised methodology for less manual data labeling their use is limited due to thefact 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.
Identifier: CFE0005178 (IID), ucf:50670 (fedora)
Note(s): 2014-05-01
M.S.
Engineering and Computer Science, Industrial Engineering and Management Systems
Masters
This record was generated from author submitted information.
Subject(s): Data Mining -- Machine Learning -- Unsupervised Learing -- Control Chart Pattern Recognition -- Clustering -- Consensus Clustering -- Ensemble Methods
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005178
Restrictions on Access: public 2014-05-15
Host Institution: UCF

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