Statistical Clustering Analysis: An Introduction - Clustering Challenges in Biological Network - page 125

The readers should be notified that although scale-based methods (also the
SBDD method) can handle slightly or moderate non-convex clusters, they are not
capable of dealing with those extremely non-convex clusters such as one cluster
is encircled by another one, as shown in Fig. 5.12. For this type of extremely
non-convex clusters, SNN method may be a good choice.
Kothari and Pitts [21] and Zhang and Albin [33] give more details of the scale-
based method and the SBDD method, respectively. Readers are also referred to
some other variants of the scale-based method to determine the number of clusters,
such as the multi-scale clustering [24], influence zones [14], SOM [4] and kernel
density estimation [29].

References

[1] M. Anderberg. Cluster analysis for applications . Academic Press, New York, 1973.

[2] A. Borst and F. Theunissen. Information theory and neural coding. Nature Neuro-

science , 2: 947-957, 1999.

[3] D. Brillinger. Nerve cell spike train data analysis: a progression of technique. Journal

of the American Statistical Association , 87: 260-271, 1992.

[4] J. Costa and M. Netto. Estimating the number of clusters in multivariate data by self-

organizing maps. International Journal of Neural Systems , 9(3): 195-202, 1999.

[5] M. Daszykowski, B. Walczak, and D. Massart. Looking for natural patterns in data

- Part 1. Density-based approach. Chemometrics and Intelligent Laboratory Systems ,
56: 83-92, 2001.

[6] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discov-

ering clusters in large spatial databases with noise. In Proc. of the 2nd Int. Conf. on
Knowledge Discovery and Data Mining (KDD-96) , pages 226-231, 1996.

[7] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. Density-based clustering in spatial

databases: the algorithm GDBSCAN and its applications. Data Mining and Knowl-
edge Discovery, An International Journal , 2(2): 169-194, 1998.

[8] L Fu and E. Medico. FLAME, a novel fuzzy clustering method for the analysis of

DNA microarray data. BMC Bioinformatics , 8: 3, 2007.

[9] J. C. Bezdek. Pattern Recogniztion with Fuzzy Objective Function Algorithms .

Plenum press, New York, 1981.

[10] C. Fraley and A. E. Raftery. How many clusters? Which clustering method? Answers

via model-based cluster analysis. Computer Journal , 41: 578-588, 1998.

[11] S. Guha, R. Rastogi, and K. Shim. CURE: an efficient clustering algorithm for large

databases. Information Systems , 26: 35-58, 2001.

[12] S. Guha, R. Rastogi, and K. Shim. ROCK: a robust clustering algorithm for categor-

ical attributes. In Proceedings of the 15th international conference on data engineer-
ing , page 512, 1999.

[13] J. Han and M. Kamber. Data Mining: Concepts and Techniques . Morgan Kaufmann

Publishers, 2005.

[14] M. Herbin, N. Bonnet, and P. Vautrot. Estimation of the number of clusters and influ-