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资源说明：数据挖掘部分算法的matlab实现,
题: [代码] 数据挖掘部分算法的matlab实现
数据挖掘部分算法的matlab实现
id3function D = ID3(train_features, train_targets, params, region)
% Classify using Quinlan's ID3 algorithm
% Inputs:
% features - Train features
% targets - Train targets
% params - [Number of bins for the data, Percentage of incorrectly assigned samples at a node]
% region - Decision region vector: [-x x -y y number_of_points]
%
% Outputs
% D - Decision sufrace
[Ni, M] = size(train_features);
%Get parameters
[Nbins, inc_node] = process_params(params);
inc_node = inc_node*M/100;
%For the decision region
N = region(5);
mx = ones(N,1) * linspace (region(1),region(2),N);
my = linspace (region(3),region(4),N)' * ones(1,N);
flatxy = [mx(, my(]';
%Preprocessing
[f, t, UW, m] = PCA(train_features, train_targets, Ni, region);
train_features = UW * (train_features - m*ones(1,M));;
flatxy = UW * (flatxy - m*ones(1,N^2));;
%First, bin the data and the decision region data
[H, binned_features]= high_histogram(train_features, Nbins, region);
[H, binned_xy] = high_histogram(flatxy, Nbins, region);
%Build the tree recursively
disp('Building tree')
tree = make_tree(binned_features, train_targets, inc_node, Nbins);
%Make the decision region according to the tree
disp('Building decision surface using the tree')
targets = use_tree(binned_xy, 1:N^2, tree, Nbins, unique(train_targets));
D = reshape(targets,N,N);
%END
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