The problem of matching data has as one of its major bottlenecks the rapid deterioration in performance of time and accuracy, as the amount of data to be processed increases. One reason for this deterioration in performance is the cost incurred by data matching systems when comparing data records to determine their similarity (or dissimilarity). Approaches such as blocking and concatenation of data attributes have been used to minimize the comparison cost. In this paper, we analyse and present Keyword and Digram clustering as alternatives for enhancing the performance of data matching systems. We compare the performance of these clustering techniques in terms of potential savings in performing comparisons and their accuracy in correctly clustering similar data. Our results on a sampled London Stock Exchange listed companies database show that using the clustering techniques can lead to improved accuracy as well as time savings in data matching systems.


Information Retrieval Minimal Span Tree Record Linkage Data Match London Stock Exchange 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Edward Tersoo Apeh
    • 1
    • 2
  • Bogdan Gabrys
    • 1
  1. 1.School of Design, Engineering and Computing, Computational Intelligence Research GroupBournemouth UniversityPooleUK
  2. 2.QGate Software LimitedFareham, HampshireUK

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