文件列表:

Corners.txt (17760, 2017-11-23)
Outlier.txt (10096, 2017-11-23)
Rcal.m (200, 2017-11-23)
clustsize.m (419, 2017-11-23)
disclust.m (263, 2017-11-23)
distance.m (119, 2017-11-23)
iris.txt (3300, 2017-11-23)
kmeans.m (5089, 2017-11-23)
maxdiam.m (337, 2017-11-23)
normalize.m (273, 2017-11-23)
random.txt (35863, 2017-11-23)
randompart.txt (14338, 2017-11-23)

This is k-means clustering where the optimal number of clusters is found by measuring Davies Bouldin index for different K's. The range of K is given by the user. Run the kmeans.m file and follow the instruction in command line. In case you have labels in your data file, it must be in the last column. If the data is 2D(i.e,having only two features), the program will automatically plot the raw data points, and the clusters in different colors.

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