文件名称：数据挖掘部分算法的matlab实现
文件大小：22KB
文件格式：TXT
更新时间：2013-05-07 09:20:31
数据挖掘部分算法的matlab实现
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