Advertisement

A Family of Novel Clustering Algorithms

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

We review the performance function associated with the familiar K-Means algorithm and that of the recently developed K-Harmonic Means. The inadequacies in these algorithms leads us to investigate a family of performance functions which exhibit superior clustering on a variety of data sets over a number of different initial conditions. In each case, we derive a fixed point algorithm for convergence by finding the fixed point of the first derivative of the performance function. We give illustrative results on a variety of data sets. We show how one of the algorithms may be extended to create a new topology-preserving mapping.

Keywords

Cluster Algorithm Performance Function Minimum Performance Fixed Point Algorithm Gaussian Basis Function 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barbakh, W., Fyfe, C.: Performance functions and clustering algorithms. Computing and Information Systems 10(2), 2–8 (2006) ISSN 1352-9404Google Scholar
  2. Bishop, C.M., Svensen, M., Williams, C.K.I.: Gtm: The generative topographic mapping. Neural Computation (1997)Google Scholar
  3. Fyfe, C.: Two topographic maps for data visualization. Data Mining and Knowledge Discovery (2006)Google Scholar
  4. Kohonen, T. (ed.): Self-Organising Maps. Springer, Heidelberg (1995)Google Scholar
  5. Pẽna, M., Fyfe, C.: Model- and data-driven harmonic topographic maps. WSEAS Transactions on Computers 4(9), 1033–1044 (2005)Google Scholar
  6. Zhang, B.: Generalized k-harmonic means – boosting in unsupervised learning. Technical report, HP Laboratories, Palo Alto (October 2000)Google Scholar
  7. Zhang, B., Hsu, M., Dayal, U.: K-harmonic means - a data clustering algorithm. Technical report, HP Laboratories, Palo Alto (October 1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.Applied Computational Intelligence Research UnitThe University of PaisleyScotland

Personalised recommendations