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An Evolutionary Based Clustering Algorithm Applied to Dada Compression for Industrial Systems

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7619)

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.

Keywords

  • hybridised clustering algorithm
  • genetic algorithms
  • K-means algorithms
  • data compression
  • sensor fault detection

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Chen, J., Mahfouf, M., Bingham, C., Zhang, Y., Yang, Z., Gallimore, M. (2012). An Evolutionary Based Clustering Algorithm Applied to Dada Compression for Industrial Systems. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_11

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  • DOI: https://doi.org/10.1007/978-3-642-34156-4_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34155-7

  • Online ISBN: 978-3-642-34156-4

  • eBook Packages: Computer ScienceComputer Science (R0)