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Contents
Common terms and phrasesadjacency matrix alpha hull Analysis Applications balls CCCD CCCD classifier centered class cover catch clusters complexity consider convex hull corresponds cover catch digraph data set defined defined in terms Definition Delaunay triangularization denote density depicted dimension dimensionality discussed disk distribution Equation estimate Euclidean example fibers Figure find finding finite first fixed function Gabriel graph greedy algorithm illustrated indicated intersection graph investigate isomap k-nearest-neighbor graph kd-tree labeled Lemma method minimum dominating set minimum spanning tree MKNN multidimensional scaling Multivariate nearest-neighbor classifier nearest-neighbor digraph neighbor node non-target nose data null hypothesis number of edges observations outliers pattern recognition plot probability problem radii radius random graph random variable random walk region of influence relative neighborhood graph Second Edition Section set of points significant simulation specific sphere digraph statistical pattern recognition subgraph subset Theorem vector vertex vertices Voronoi diagram Voronoi tessellation References to this bookFrom other books
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