An on-line agglomerative clustering method for nonstationary data
Neural Computation
Data Mining for Features Using Scale-Sensitive Gated Experts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles
Machine Learning - Special issue: Unsupervised learning
Modified fuzzy C-means algorithm for cellular manufacturing
Fuzzy Sets and Systems
A unified framework for model-based clustering
The Journal of Machine Learning Research
Object-Based Image Analysis Using Multiscale Connectivity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining and Knowledge Discovery
Data Clustering Using a Model Granular Magnet
Neural Computation
Gaussian Mean-Shift Is an EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A NON-PARAMETRIC APPROACH TO SIMPLICITY CLUSTERING
Applied Artificial Intelligence
On the number of modes of a Gaussian mixture
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Outlier detection with streaming dyadic decomposition
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Regularized discriminant entropy analysis
Pattern Recognition
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We derive a new clustering algorithm based on information theoryand statistical mechanics, which is the only algorithm thatincorporates scale. It also introduces a new concept intoclustering: cluster independence. The cluster centers correspond tothe local minima of a thermodynamic free energy, which areidentified as the fixed points of a one-parameter nonlinear map.The algorithm works by melting the system to produce a tree ofclusters in the scale space. Melting is also insensitive tovariability in cluster densities, cluster sizes, and ellipsoidalshapes and orientations. We tested the algorithm successfully onboth simulated data and a Synthetic Aperture Radar image of anagricultural site with 12 attributes for crop identification.