Input: number of clusters k, and n data object contains a database. Output: meet the standard minimum variance k-clustering. Processes: (1) n data objects from arbitrarily selected k object as initial cluster centers. (2) based on the mean of each cluster object (central object), calculated for each object and the distance to the object of these centers and according to the minimum distance to re-divide the corresponding object (3) re-calculated for each (a change) clustering means (central object) (4) Cycle (2) to (3) until no further change in each cluster until the

KMEANS
......\Kmeans.java

• We are an exchange download platform that only provides communication channels. The downloaded content comes from the internet. Except for download issues, please Google on your own.
• The downloaded content is provided for members to upload. If it unintentionally infringes on your copyright, please contact us.
• Please use Winrar for decompression tools
• If download fail, Try it againg or Feedback to us.
• If downloaded content did not match the introduction, Feedback to us，Confirm and will be refund.
• Before downloading, you can inquire through the uploaded person information

Nothing．