kmeans 初步学习小结

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');