Description: k-means algorithm accepts parameters k n and the previously input data is divided into k-clustering objects in order to make the obtained cluster met: the same high similarity clustering objects objects and different clustering Similarity small. The use of the cluster similarity clustering objects obtained by a mean of " central object" (center of gravity) to be calculated for.
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src
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__MACOSX
........\src
........\...\._.DS_Store
src\Kahans.java
...\KMeans.java
__MACOSX\src\._KMeans.java
src\test.java
...\vector.java
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