The Impact of Cluster Stability on Class Decomposition in Antibody Display Data

Inese Polaka, Arkady Borisov


This article focuses on cluster stability evaluation to assess the characteristics of the dataset and the subclasses found in class decomposition. The evaluation is an iterative process, making small changes to the dataset in every step and reapplying the cluster analysis. These small changes (removing one object from the dataset is repeated for 20 iterations in this case) should not have any impact on clusters if they are stable (meaning that other objects that were not removed stay in the same clusters as in the full clustering).


Сlass decomposition; clustering; cluster stability; data mining

Full Text:



Cancer Program Data Sets. [Online.] Available: [Accessed September 14, 2012].

J. H. Ward, Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association Vol. 58, Issue 301, 1963, pp. 236–244.

J. R. Quinlan, C4.5: Programs for Machine Learning. San Francisco, CA: Morgan Kaufmann Publishers, 1993. 302 p.

L. Breiman, Random Forests, Machine Learning Vol. 45, Issue 1, 2001, pp. 5-32.

V. N. Vapnik, The Nature of Statistical Learning Theory. Berlin: Springer-Verlag, 1995.188 p.

G. H. John, P. Langley, Estimating Continuous Distributions in Bayesian Classifiers, Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 1995, pp. 338-345.

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H. Witten, The WEKA Data Mining Software: An Update, SIGKDD Explorations, Vol. 11, Issue 1, 2009, pp. 10-18.


  • There are currently no refbacks.

Copyright (c) 2012 Inese Polaka, Arkady Borisov

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.