Abstract
We investigate the contribution of unsupervised learning to identify patient’s profiles suffering from addictions. We propose a new clustering approach based on coupling b-coloring of graph and Bregman hard clustering algorithm in order to automatically find the number of categories or groups of patients and the ”best” representative patients’ profile of each group. The study was carried out in close collaboration with the French co-operative health organization called the ”Centre Mutualiste d’Addictologie”, an aftercare centre for addictions. The quantitative data arises from a cohort of seven different aftercare centres for addiction located in France. The study concerns 301 patients suffering from dependence (addictions with psychoactive substances and/or behaviour addictions).
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
Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J.: Clustering with Bregman divergences. Journal of Machine Learning Research 6, 1705–1749 (2005)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(4), 224–227 (1979)
Diestel, R.: Graph Theory. Springer, Heidelberg (2006)
Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. Journal of Cybernetics 4(1), 95–104 (1974)
Elghazel, H., Deslandres, V., Hacid, M.-S., Dussauchoy, A., Kheddouci, H.: A new clustering approach for symbolic data and its validation: Application to the healthcare data. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 473–482. Springer, Heidelberg (2006)
Elghazel, H., Kheddouci, H., Deslandres, V., Dussauchoy, A.: A Graph b-coloring Framework for Data Clustering. Journal of Mathematical Modelling and Algorithms 7(4), 389–423 (2008)
Irving, W., Manlove, D.F.: The b-chromatic number of a graph. Discrete Applied Mathematics 91, 127–141 (1999)
Li, T., Ma, S., Ogihara, M.: Entropy-Based Criterion in Categorical Clustering. In: Proceedings of the 21st International Conference on Machine Learning, Banff, Canada (2004)
Ogino, H., Yoshida, T.: Toward Improving Re-coloring Based Clustering with Graph b-Coloring. In: Zhang, B.-T., Orgun, M.A. (eds.) PRICAI 2010. LNCS, vol. 6230, pp. 206–218. Springer, Heidelberg (2010)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 53–65 (1987)
Walesiak, M.: The Generalized Distance Measure. In: Multidimensional Statistical Analysis. Wrocław Academy of Commerce, Wrocław (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Combes, C. (2014). Cluster Analysis of Patients Suffering from Addictions. In: Bajo Perez, J., et al. Trends in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection. Advances in Intelligent Systems and Computing, vol 293. Springer, Cham. https://doi.org/10.1007/978-3-319-07476-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-07476-4_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07475-7
Online ISBN: 978-3-319-07476-4
eBook Packages: EngineeringEngineering (R0)