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Clustering Algorithm for Travel Distance Analysis

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  • Zenina Nadezda
  • Borisov Arkady

    (Riga Technical University)

Abstract

An important problem in the application of cluster analysis is the decision regarding how many clusters should be derived from the data. The aim of the paper is to determine a number of clusters with a distinctive breaking point (elbow), calculating variance ratio criterion (VRC) by Calinski and Harabasz and J-index in order to check robustness of cluster solutions. Agglomerative hierarchical clustering was used to group a data set that is characterized by a complex structure, which makes it difficult to identify a structure of homogeneous groups. Stability of cluster solutions was performed by using different similarity measures and reordering cases in the dataset.

Suggested Citation

  • Zenina Nadezda & Borisov Arkady, 2013. "Clustering Algorithm for Travel Distance Analysis," Information Technology and Management Science, Sciendo, vol. 16(1), pages 85-88, December.
    • Handle: RePEc:vrs:itmasc:v:16:y:2013:i:1:p:85-88:n:13
    DOI: 10.2478/itms-2013-0013
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    References listed on IDEAS

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    1. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    2. Anable, Jillian, 2005. "'Complacent Car Addicts' or 'Aspiring Environmentalists'? Identifying travel behaviour segments using attitude theory," Transport Policy, Elsevier, vol. 12(1), pages 65-78, January.
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