Algorithms for clustering data
Algorithms for clustering data
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
Data clustering using a model granular magnet
Neural Computation
Inference of Surfaces, 3D Curves, and Junctions from Sparse, Noisy, 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Curvilinear Features in Spatial Point Patterns: Principal Curve Clustering with Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Texture Segmentation by Multiscale Aggregation of Filter Responses and Shape Elements
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Astronomical Image and Data Analysis (Astronomy and Astrophysics Library)
Astronomical Image and Data Analysis (Astronomy and Astrophysics Library)
Fast k most similar neighbor classifier for mixed data (tree k-MSN)
Pattern Recognition
Multilevel space-time aggregation for bright field cell microscopy segmentation and tracking
Journal of Biomedical Imaging - Special issue on mathematical methods for images and surfaces
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We present a novel multiscale clustering algorithm inspired by algebraic multigrid techniques. Our method begins with assembling data points according to local similarities. It uses an aggregation process to obtain reliable scale-dependent global properties, which arise from the local similarities. As the aggregation process proceeds, these global properties affect the formation of coherent clusters. The global features that can be utilized are for example density, shape, intrinsic dimensionality and orientation. The last three features are a part of the manifold identification process which is performed in parallel to the clustering process. The algorithm detects clusters that are distinguished by their multiscale nature, separates between clusters with different densities, and identifies and resolves intersections between clusters. The algorithm is tested on synthetic and real data sets, its running time complexity is linear in the size of the data set.