接触kmeans 算法比较长时间了，但是一直没好好明白怎么回事。推荐几个好点的链接。
http://coolshell.cn/articles/7779.html
http://blog.csdn.net/zouxy09/article/details/9982495
http://www.360doc.com/content/13/1122/14/10724725_331295214.shtml
运用matlab函数的一个最基本程序
yangben= load('F:\iris.txt'); s=size(yangben); hang=s(1); lie=s(2); x=yangben(:,1:4); opts=statset('Display','final'); k=3; [idx,ctrs]=kmeans(x,k,'Distance','city','Replicates',5,'options',opts); plot(x(idx==1,1),x(idx==1,2),'r.',... x(idx==2,1),x(idx==2,2),'b.',... x(idx==3,1),x(idx==3,2),'g.'); ctrs(:,1),ctrs(:,2),ctrs(:,3),'kx';总的来说，还可以，是因为数据集比较权威。
贴上help里kmeans 的帮助文档，以后再研究。暂且就会用这个函数就行了。看完这个，终于可以好好看看稀疏编码进行特征提取的问题了。
help kmeans kmeans K-means clustering. IDX = kmeans(X, K) partitions the points in the N-by-P data matrix X into K clusters. This partition minimizes the sum, over all clusters, of the within-cluster sums of point-to-cluster-centroid distances. Rows of X correspond to points, columns correspond to variables. Note: when X is a vector, kmeans treats it as an N-by-1 data matrix, regardless of its orientation. kmeans returns an N-by-1 vector IDX containing the cluster indices of each point. By default, kmeans uses squared Euclidean distances. kmeans treats NaNs as missing data, and ignores any rows of X that contain NaNs. [IDX, C] = kmeans(X, K) returns the K cluster centroid locations in the K-by-P matrix C. [IDX, C, SUMD] = kmeans(X, K) returns the within-cluster sums of point-to-centroid distances in the 1-by-K vector sumD. [IDX, C, SUMD, D] = kmeans(X, K) returns distances from each point to every centroid in the N-by-K matrix D. [ ... ] = kmeans(..., 'PARAM1',val1, 'PARAM2',val2, ...) specifies optional parameter name/value pairs to control the iterative algorithm used by kmeans. Parameters are: 'Distance' - Distance measure, in P-dimensional space, that kmeans should minimize with respect to. Choices are: 'sqEuclidean' - Squared Euclidean distance (the default) 'cityblock' - Sum of absolute differences, a.k.a. L1 distance 'cosine' - One minus the cosine of the included angle between points (treated as vectors) 'correlation' - One minus the sample correlation between points (treated as sequences of values) 'Hamming' - Percentage of bits that differ (only suitable for binary data) 'Start' - Method used to choose initial cluster centroid positions, sometimes known as "seeds". Choices are: 'sample' - Select K observations from X at random (the default) 'uniform' - Select K points uniformly at random from the range of X. Not valid for Hamming distance. 'cluster' - Perform preliminary clustering phase on random 10% subsample of X. This preliminary phase is itself initialized using 'sample'. matrix - A K-by-P matrix of starting locations. In this case, you can pass in [] for K, and kmeans infers K from the first dimension of the matrix. You can also supply a 3D array, implying a value for 'Replicates' from the array's third dimension. 'Replicates' - Number of times to repeat the clustering, each with a new set of initial centroids. A positive integer, default is 1. 'EmptyAction' - Action to take if a cluster loses all of its member observations. Choices are: 'error' - Treat an empty cluster as an error (the default) 'drop' - Remove any clusters that become empty, and set the corresponding values in C and D to NaN. 'singleton' - Create a new cluster consisting of the one observation furthest from its centroid. 'Options' - Options for the iterative algorithm used to minimize the fitting criterion, as created by STATSET. Choices of STATSET parameters are: 'Display' - Level of display output. Choices are 'off', (the default), 'iter', and 'final'. 'MaxIter' - Maximum number of iterations allowed. Default is 100. 'OnlinePhase' - Flag indicating whether kmeans should perform an "on-line update" phase in addition to a "batch update" phase. The on-line phase can be time consuming for large data sets, but guarantees a solution that is a local minimum of the distance criterion, i.e., a partition of the data where moving any single point to a different cluster increases the total sum of distances. 'on' (the default) or 'off'. Example: X = [randn(20,2)+ones(20,2); randn(20,2)-ones(20,2)]; opts = statset('Display','final'); [cidx, ctrs] = kmeans(X, 2, 'Distance','city', ... 'Replicates',5, 'Options',opts); plot(X(cidx==1,1),X(cidx==1,2),'r.', ... X(cidx==2,1),X(cidx==2,2),'b.', ctrs(:,1),ctrs(:,2),'kx');
转载于:https://www.cnblogs.com/natalie/p/4794946.html
