A non-parametric hierarchical clustering model.

Mohamad, S., Bouchachia, A. and Sayed-Mouchaweh, M., 2016. A non-parametric hierarchical clustering model. In: 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), 1-3 December 2015, Douai, France.

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DOI: 10.1109/EAIS.2015.7368803

Abstract

© 2015 IEEE. We present a novel non-parametric clustering model using Gaussian mixture model (NHCM). NHCM uses a novel Dirichlet process (DP) prior allowing for more flexible modeling of the data, where the base distribution of DP is itself an infinite mixture of Gaussian conjugate prior. NHCM can be thought of as hierarchical clustering model, in which the low level base prior governs the distribution of the data points forming sub-clusters, and the higher level prior governs the distribution of the sub-clusters forming clusters. Using this hierarchical configuration, we can maintain low complexity of the model and allow for clustering skewed complex data. To perform inference, we propose a Gibbs sampling algorithm. Empirical investigations have been carried out to analyse the efficiency of the proposed clustering model.

Item Type:Conference or Workshop Item (Paper)
Additional Information:© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Group:Faculty of Science & Technology
ID Code:29870
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:18 Oct 2017 13:10
Last Modified:18 Oct 2017 13:10

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