An evolutionary based clustering algorithm applied to dada compression for industrial systems

Authors:
Jun Chen;Mahdi Mahfouf;Chris Bingham;Yu Zhang;Zhijing Yang;Michael Gallimore
Affiliations:
School of Engineering, University of Lincoln, Lincoln, U.K.;Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, U.K.;School of Engineering, University of Lincoln, Lincoln, U.K.;School of Engineering, University of Lincoln, Lincoln, U.K.;School of Engineering, University of Lincoln, Lincoln, U.K.;School of Engineering, University of Lincoln, Lincoln, U.K.
Venue:
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Year:
2012

Citing 7
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Abstract

In this paper, in order to address the well-known 'sensitivity' problems associated with K-means clustering, a real-coded Genetic Algorithms (GA) is incorporated into K-means clustering. The result of the hybridisation is an enhanced search algorithm obtained by incorporating the local search capability rendered by the hill-climbing optimisation with the global search ability provided by GAs. The proposed algorithm has been compared with other clustering algorithms under the same category using an artificial data set and a benchmark problem. Results show, in all cases, that the proposed algorithm outperforms its counterparts in terms of global search capability. Moreover, the scalability of the proposed algorithm to high-dimensional problems featuring a large number of data points has been validated using an application to compress field data sets from sub-15MW industry gas turbines, during commissioning. Such compressed field data is expected to result in more efficient and more accurate sensor fault detection.