NDLI: Unsupervised learning of arbitrarily shaped clusters using ensembles of Gaussian models

Content Provider	Springer Nature Link
Author	Frigui, Hichem
Copyright Year	2005
Abstract	We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscillator and uses the relative similarity between the points to model the interaction between the oscillators. SyMP is robust to noise and outliers, determines the number of clusters in an unsupervised manner, and identifies clusters of arbitrary shapes. The robustness of SyMP is an intrinsic property of the synchronization mechanism. To determine the optimum number of clusters, SyMP uses a dynamic and cluster dependent resolution parameter. To identify clusters of various shapes, SyMP models each cluster by an ensemble of Gaussian components. SyMP does not require the specification of the number of components for each cluster. This number is automatically determined using a dynamic intra-cluster resolution parameter. Clusters with simple shapes would be modeled by few components while clusters with more complex shapes would require a larger number of components. The proposed clustering approach is empirically evaluated with several synthetic data sets, and its performance is compared with GK and CURE. To illustrate the performance of SyMP on real and high-dimensional data sets, we use it to categorize two image databases.
Starting Page	32
Ending Page	49
Page Count	18
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	8
Issue Number	1-2
e-ISSN	1433755X
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-08-09
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Synchronization Robust clustering Gaussian mixture models Image database categorization
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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