文件列表:

clustering_code_v02\Calc_Inv.m (2000, 2005-09-06)
clustering_code_v02\Classify_New_Data.m (4971, 2005-09-06)
clustering_code_v02\Cluster_Cost.m (2059, 2005-09-06)
clustering_code_v02\Digits_Data.mat (1641856, 2005-09-06)
clustering_code_v02\Digits_Data_us.mat (821072, 2005-09-06)
clustering_code_v02\Find_Clusters.m (1799, 2005-09-06)
clustering_code_v02\Gen_Scale.m (1075, 2005-09-06)
clustering_code_v02\LG_Cluster.m (4109, 2005-09-06)
clustering_code_v02\Moon_Data.mat (4040, 2005-09-06)
clustering_code_v02\Opt_Alpha_Sigma.m (3829, 2005-09-06)
clustering_code_v02\Set_Default_Learning_Paramters.m (2417, 2005-09-06)
clustering_code_v02\batch_3_Spiral_3D.m (2576, 2005-09-06)
clustering_code_v02\batch_Digits.m (4207, 2005-09-06)
clustering_code_v02\batch_Digits_unseen.m (2213, 2005-09-06)
clustering_code_v02\batch_test_toys.m (10303, 2005-09-06)
clustering_code_v02\cost_alpha_sigma.m (1826, 2005-09-06)
clustering_code_v02\distance_matrix_mex.c (1132, 2005-09-06)
clustering_code_v02\distance_matrix_mex.dll (20480, 2005-09-06)
clustering_code_v02\error_thing.m (1613, 2005-09-06)
clustering_code_v02\medianDistance.m (1066, 2005-09-06)
clustering_code_v02\dst.mat (1143256, 2006-07-02)
clustering_code_v02 (0, 2006-07-02)

Clustering Through Ranking On Manifolds Version 0.2 Copyright by Markus Breitenbach and Gregory Z. Grudic This code is for your personal and research use only. http://www.cs.colorado.edu/~grudic/ http://ucsu.colorado.edu/~breitenm/ This software is provided "as is," without warranty of any kind, express or implied. In no event shall the authors be held liable for any direct, indirect, incidental, special or consequential damages arising out of the use of or inability to use this software. LG_Cluster.m - Clustering of Data Classify_New_Data.m - Classification of new data (out-of-sample) Set_Default_Learning_Paramters.m - Set learning parameters batch_3_Spiral_3D.m - A toy-data example (3 spirals) batch_test_toys.m - A toy-data example batch_two_moon_toy_stuff.m error_thing.m - Compute the error-rate for clustering The model is a struct that looks like this: DST_TYPE: 1 -- DST type (Euclidean / Dot-product) SCALE: 1 -- Data scaled? A: [256x1 double] -- Scaling... B: [256x1 double] Class_Outlier: [1x4 struct] -- Outliers for each class F_orig: [800x4 double] -- Original F matrix (columns selected using inc_clust) F_norm: [800x4 double] -- scaled F matrix (columns selected using inc_clust) Y: [800x4 double] -- Labels for the data (a 1 in a column i indicating that the case is in class i) Y_orig: [800x4 double] -- same thing, but a different underlying implementation mean_dist: [1x800 double] -- mean distance of matrix D_M sort_mean_dist: [1x800 double] -- sorted... ind_sort_mean_dist: [1x800 double] -- the indices of the sorted matrix D_M - use for Outlier Detection S_norm: [800x800 double] -- normalized S matrix ind_mat_unseen: [800x800 double] -- precomputed for unseen data one_over_2_sigma_sq: 0.3953 -- one over 2 sigma squared... X: [800x256 double] -- the training data Dt: [1x800 double] -- Diagonal of W num_classes: 4 -- Number of clusters norm_iaS_inv: [800x800 double] -- normalized F matrix iaS_inv: [800x800 double] -- F matrix my_alpha: 0.9949 -- Alpha my_sigma: 1.1246 -- Sigma ind_clust: [4x1 double] -- the inidices for the centers model.Class_Outlier is a struct containing: val -- ind -- logical indicating class membership of the case sort_val -- ind_sort -- indices of worst outliers - use for Outlier Detection plot_values -- Known bugs: -the optimization procedure got reimplemented on the way and the results for USPS got slightly worse. This is currently under investigation.

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