ACM Computing Surveys (CSUR)
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expansive and Competitive Learning for Vector Quantization
Neural Processing Letters
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
A Knowledge-Based Neurocomputing Approach to Extract Refined Linguistic Rules from Data
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Self-Organizing-Map Based Clustering Using a Local Clustering Validity Index
Neural Processing Letters
A unified framework for image database clustering and content-based retrieval
Proceedings of the 2nd ACM international workshop on Multimedia databases
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Clustering Irregular Shapes Using High-Order Neurons
Neural Computation
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
Multiorder neurons for evolutionary higher-order clustering and growth
Neural Computation
On the use of divergence distance in fuzzy clustering
Fuzzy Optimization and Decision Making
Improved Production of Competitive Learning Rules with an Additional Term for Vector Quantization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A Novel Elliptical Basis Function Neural Networks Model Based on a Hybrid Learning Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Data Clustering with Semi-binary Nonnegative Matrix Factorization
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Clustering: A neural network approach
Neural Networks
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A new approach to clustering data with arbitrary shapes
Pattern Recognition
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
An N-parallel multivalued network: applications to the travelling salesman problem
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Image segmentation using histogram fitting and spatial information
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Integration of ant colony SOM and k-means for clustering analysis
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Privacy-preserving SOM-based recommendations on horizontally distributed data
Knowledge-Based Systems
A comparative study of efficient initialization methods for the k-means clustering algorithm
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
A sample-based hierarchical adaptive K-means clustering method for large-scale video retrieval
Knowledge-Based Systems
QUAC: Quick unsupervised anisotropic clustering
Pattern Recognition
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We propose a self-organizing network for hyperellipsoidal clustering (HEC). It consists of two layers. The first employs a number of principal component analysis subnetworks to estimate the hyperellipsoidal shapes of currently formed clusters. The second performs competitive learning using the cluster shape information from the first. The network performs partitional clustering using the proposed regularized Mahalanobis distance, which was designed to deal with the problems in estimating the Mahalanobis distance when the number of patterns in a cluster is less than or not considerably larger than the dimensionality of the feature space during clustering. This distance also achieves a tradeoff between hyperspherical and hyperellipsoidal cluster shapes so as to prevent the HEC network from producing unusually large or small clusters. The significance level of the Kolmogorov-Smirnov test on the distribution of the Mahalanobis distances of patterns in a cluster to the cluster center under the Gaussian cluster assumption is used as a compactness measure. The HEC network has been tested on a number of artificial data sets and real data sets, We also apply the HEC network to texture segmentation problems. Experiments show that the HEC network leads to a significant improvement in the clustering results over the K-means algorithm with Euclidean distance. Our results on real data sets also indicate that hyperellipsoidal shaped clusters are often encountered in practice