Keywords

Data mining, machine learning, unsupervised learing, control chart pattern recognition, clustering, consensus clustering, ensemble methods

Abstract

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.

Notes

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Graduation Date

2014

Semester

Spring

Advisor

Xanthopoulos, Petros

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering; Systems Engineering Track

Format

application/pdf

Identifier

CFE0005178

URL

http://purl.fcla.edu/fcla/etd/CFE0005178

Language

English

Release Date

May 2014

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic

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