NDLI: Semi-supervised Artificial Immune Mixture Models Clustering

Content Provider	IET Digital Library
Author	Huang, Wenlong Wang, Xiaodan Li, Guohong Kang, Hongxia
Abstract	Learning with partly labeled data aims at combining labeled and unlabeled data in order to boost the accuracy of an algorithm. The traditional Expectation maximization (EM) algorithm only produces locally optimal solutions, it is sensitive to initialization, and the number of components of mixture model must be known in advance. We propose a novel semi-supervised clustering algorithm that uses Gaussian mixture models (GMM) as the underlying clustering model. A novel adaptive global search mechanism is introduced into semi-supervised gaussian mixture model-based clustering, where the EM algorithm is incorporated with the ideas of an immune clonal selection technique. The new algorithm overcomes the various problems associated with the traditional EM algorithm. And it can improve the effectiveness in estimating the parameters and determining the optimal number of clusters automatically. The experimental results illustrate the proposed algorithm provides significantly better clustering results, when compared with other methods of incorporating equivalence constraints.
Starting Page	249
Ending Page	255
Page Count	7
ISSN	10224653
Volume Number	25
e-ISSN	20755597
Issue Number	Issue 2, Mar (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/25/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2016.03.009
Journal	Chinese Journal of Electronics
Publisher Date	2016-03-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Adaptive Global Search Mechanism Data Handling Technique Equivalence Constraint Gaussian Mixture Model-based Clustering Gaussian Processes Knowledge Engineering Technique Learning in AI Mixture Model Modelling And Identification Parameter Estimation Pattern Clustering Semisupervised Clustering Algorithm Simulation Statistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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