Semi-supervised Gaussian Mixture Models Clustering Algorithm Based on Immune Clonal Selection
- DOI
- 10.2991/nceece-15.2016.214How to use a DOI?
- Keywords
- Semi-supervised clustering; Gaussian Mixture Models; immune clonal selection.
- Abstract
Semi-supervised Clustering with constraints is an active area of machine learning and data mining research.. Shental used the Expectation Maximization (EM) procedure to handle semi-supervised Gaussian Mixture Models (GMM) estimation, in which positive and negative constraints are incorporated with to improve clustering results. However the conventional EM algorithm only produces solutions that are locally optimal, and thus may not achieve the globally optimal solution, and it is sensitive to initialization, moreover, the number of components of mixture model must be known in advance. This paper introduces the artificial immune clonal selection algorithm into semi-supervised GMM-based clustering techniques, where the EM algorithm is incorporated with the ideas of a clonal selection algorithm. The new algorithm overcomes the various problems associated with the traditional EM algorithm. It can improve the effectiveness in estimating the parameters and determining simultaneously the optimal number of clusters automatically. The experimental results illustrate the proposed clustering algorithm provides significantly better clustering results.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Wenlong Huang AU - Xiaodan Wang PY - 2015/12 DA - 2015/12 TI - Semi-supervised Gaussian Mixture Models Clustering Algorithm Based on Immune Clonal Selection BT - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering PB - Atlantis Press SP - 1204 EP - 1210 SN - 2352-5401 UR - https://doi.org/10.2991/nceece-15.2016.214 DO - 10.2991/nceece-15.2016.214 ID - Huang2015/12 ER -