Abstract
In this paper, we combine two concepts. The first is the Evidence Accumulation Clustering framework, which uses a voting scheme to combine clustering ensembles and produce a co-association matrix. The second concept are Dissimilarity Increments, which are a high order dissimilarity measure which can identify sparse clusters, since it uses three data points at a time instead of two points, as in Euclidean distance. These two concepts are combined to form a new family of clustering algorithms, where the co-association matrix is used to form a distance which is then used to compute dissimilarity increments. These clustering algorithms are shown to improve the clustering results when compared to the usual Evidence Accumulation Clustering framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aidos, H., Fred, A.: Hierarchical clustering with high order dissimilarities. In: Proc. Int. Conf. on Machine Learning and Data Mining (MLDM 2011), pp. 280–293 (2011)
Aidos, H., Fred, A.: Statistical modeling of dissimilarity increments for d-dimensional data: Application in partitional clustering. Pattern Recognition 45(9), 3061–3071
Ayad, H.G., Kamel, M.S.: Cluster-based cumulative ensembles. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 236–245. Springer, Heidelberg (2005)
Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 381–396
Fred, A.: Finding consistent clusters in data partitions. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 309–318. Springer, Heidelberg (2001)
Fred, A., Jain, A.: Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 835–850
Fred, A., Leitão, J.: A new cluster isolation criterion based on dissimilarity increments. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(8), 944–958
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer Series in Statistics. Springer (2009)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323
Kuncheva, L.I., Hadjitodorov, S.T.: Using diversity in cluster ensembles. In: Proc. Int. Conf. on Systems, Man and Cybernetics, pp. 1214–1219
Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3, 583–617
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Elsevier Academic Press (2009)
Ueda, N., Nakano, R., Ghahramani, Z., Hinton, G.E.: SMEM algorithm for mixture models. Neural Computation 12(9), 2109–2128 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aidos, H., Fred, A. (2013). Dissimilarity Increments Distribution in the Evidence Accumulation Clustering Framework. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_63
Download citation
DOI: https://doi.org/10.1007/978-3-642-38628-2_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
eBook Packages: Computer ScienceComputer Science (R0)