Paper Info
Title
An evolutionary based clustering algorithm applied to dada compression for industrial systems
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.
Year
DOI
Venue
2012
10.1007/978-3-642-34156-4_11
IDA
Keywords
Field
DocType
dada compression,compress field data set,artificial data,industrial system,field data,data point,enhanced search algorithm,k-means clustering,global search ability,clustering algorithm,proposed algorithm,global search capability,genetic algorithms,data compression
Data mining,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Search algorithm,Correlation clustering,Computer science,Determining the number of clusters in a data set,Artificial intelligence,Cluster analysis,DBSCAN,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
6
Name
Order
Citations
PageRank
Jun Chen113.41
Mahdi Mahfouf223533.17
Chris Bingham300.34
Yu Zhang429498.00
Zhijing Yang513425.25
Michael Gallimore612.77