Current Search: Data Mining -- Machine Learning -- Unsupervised Learing -- Control Chart Pattern Recognition -- Clustering -- Consensus Clustering -- Ensemble Methods (x)
-
-
Title
-
An Unsupervised Consensus Control Chart Pattern Recognition Framework.
-
Creator
-
Haghtalab, Siavash, Xanthopoulos, Petros, Pazour, Jennifer, Rabelo, Luis, University of Central Florida
-
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...
Show moreEarly 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.
Show less
-
Date Issued
-
2014
-
Identifier
-
CFE0005178, ucf:50670
-
Format
-
Document (PDF)
-
PURL
-
http://purl.flvc.org/ucf/fd/CFE0005178
