Pattern recognition: human and mechanical
Pattern recognition: human and mechanical
A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Review of neural networks for speech recognition
Neural Computation
Entropy and information theory
Entropy and information theory
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Elements of information theory
Elements of information theory
Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Clustering Algorithms
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Approach to Speaker Identification Using Multiple Classifiers
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A new clustering evaluation function using Renyi's information potential
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
A New Cluster Isolation Criterion Based on Dissimilarity Increments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Information Clustering Algorithm
Neural Computation
Enhanced methods in computer security, biometric and artificial intelligence systems
From error probability to information theoretic (multi-modal) signal processing
Signal Processing - Special issue: Information theoretic signal processing
Information cut for clustering using a gradient descent approach
Pattern Recognition
Analytically tractable case of fuzzy c-means clustering
Pattern Recognition
Neighbor number, valley seeking and clustering
Pattern Recognition Letters
Kernel-Based Positioning in Wireless Local Area Networks
IEEE Transactions on Mobile Computing
LEGClust—A Clustering Algorithm Based on Layered Entropic Subgraphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Regularized Clustering Algorithm Based on Calculus of Variations
Journal of Signal Processing Systems
Feature Discovery by Enhancement and Relaxation of Competitive Units
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Enhancing and Relaxing Competitive Units for Feature Discovery
Neural Processing Letters
Data Mining and Knowledge Discovery
Universal Estimation of Information Measures for Analog Sources
Foundations and Trends in Communications and Information Theory
Computational Statistics & Data Analysis
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Low complexity blind equalization based on parzen window method
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Centroid neural network with Bhattacharyya kernel for GPDF data clustering
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Identifying the underlying hierarchical structure of clusters in cluster analysis
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Generalized clustering via kernel embeddings
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Compressive sensing based positioning using RSS of WLAN access points
INFOCOM'10 Proceedings of the 29th conference on Information communications
The mee principle in data classification: A perceptron-based analysis
Neural Computation
Clustering using elements of information theory
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Canonical sequence extraction and HMM model building based on hierarchical clustering 1
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Theory of a probabilistic-dependence measure of dissimilarity among multiple clusters
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Classification of MPEG VBR video data using gradient-based FCM with divergence measure
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Determining the number of clusters using information entropy for mixed data
Pattern Recognition
Optimizing the cauchy-schwarz PDF distance for information theoretic, non-parametric clustering
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
The Journal of Machine Learning Research
Efficient selection of multiple objects on a large scale
Proceedings of the 18th ACM symposium on Virtual reality software and technology
Optimized bi-dimensional data projection for clustering visualization
Information Sciences: an International Journal
Journal of Information Science
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Clustering is one of the important topics in pattern recognition. Since only the structure of the data dictates the grouping (unsupervised learning), information theory is an obvious criteria to establish the clustering rule. This paper describes a novel valley seeking clustering algorithm using an information theoretic measure to estimate the cost of partitioning the data set. The information theoretic criteria developed here evolved from a Renyi's entropy estimator that was proposed recently and has been successfully applied to other machine learning applications. An improved version of the k-change algorithm is used in optimization because of the stepwise nature of the cost function and existence of local minima. Even when applied to nonlinearly separable data, the new algorithm performs well, and was able to find nonlinear boundaries between clusters. The algorithm is also applied to the segmentation of magnetic resonance imaging data (MRI) with very promising results.