A practical application of cluster analysis using SPSS

  • Kim, Dae-Hak (School of Computer & Information Communication Engineering, Catholic University of Daegu)
  • Published : 2009.11.30

Abstract

Basic objective in cluster analysis is to discover natural groupings of items or variables. In general, clustering is conducted based on some similarity (or dissimilarity) matrix or the original input text data. Various measures of similarities (or dissimilarities) between objects (or variables) are developed. We introduce a real application problem of clustering procedure in SPSS when the distance matrix of the objects (or variables) is only given as an input data. It will be very helpful for the cluster analysis of huge data set which leads the size of the proximity matrix greater than 1000, particularly. Syntax command for matrix input data in SPSS for clustering is given with numerical examples.

References

  1. Cho, M. and Kim, S. (2008). Comparative study on statistical packages analyzing survival model- SAS, SPSS, STATA. Journal of Korean Data & Information Science Society, 19, 487-496.
  2. Cole, S. R. (1999). Simple bootstrap statistical inference using the SAS system. Computer Methods and Programs in Biomedicine, 60, 79-82. https://doi.org/10.1016/S0169-2607(99)00016-4
  3. Dillon, W. R. and Goldstein, M. (1984). Multivariate analysis: Methods and applications, John Wiley & Sons.
  4. Jain, A. K. and Moreau, J. V. (1987). Bootstrap technique in cluster analysis. Pattern Recognition, 20, 547-568. https://doi.org/10.1016/0031-3203(87)90081-1
  5. Johnson, R. A. and Wichern, D. W. (1982). Applied multivariate statistical analysis, Prentice Hall, Engel-wood Cliffs, New Jersey.
  6. Lee, K. (2004). Curve clustering in microarray. Journal of Korean Data & Information Science Society, 15, 575-584.
  7. SPSS (2004). SPSS Advanced Models 12.0.1, SPSS Inc., Chicago.
  8. Thomas, B. and Timothy, K. (1987). Comparing statistical packages: SPSS, Biomed and SAS. The Social Science Journal, 24, 329-336. https://doi.org/10.1016/0362-3319(87)90080-2