Difference between revisions of "K-Means"

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Our GPU implementation is based on the k-means implementation of Northwestern University's Minebench The source code of  
 
Our GPU implementation is based on the k-means implementation of Northwestern University's Minebench The source code of  
serial/OpenMP version of k-means which is developed by Minebench team can be downloaded from [http://cucis.ece.northwestern.edu/projects/DMS/MineBench.html link1] and [www.people.virginia.edu/~sc5nf/kmeans.zip link2].
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serial/OpenMP version of k-means which is developed by Minebench team can be downloaded from [http://cucis.ece.northwestern.edu/projects/DMS/MineBench.html link1] and [http://www.people.virginia.edu/~sc5nf/kmeans.zip link2].
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Our CUDA implementation can be downloaded from [http://www.people.virginia.edu/~sc5nf/K_Means.zip here]. This version is used for our [http://www.people.virginia.edu/~sc5nf/GPGPU_workshop.pdf GPGPU workshop paper].  A new version with more speedup and several optimizations will be released soon.

Revision as of 17:05, 1 July 2008

K-means is a clustering algorithm used extensively in data-mining and elsewhere, important primarily for its simplicity. Many data-mining algorithms show a high degree of data parallelism.

In k-means, a data object is comprised of several values, called features. By dividing a cluster of data objects into K sub-clusters, k-means represents all the data objects by the mean values or centroids of their respective sub-clusters. The initial cluster center for each sub-cluster is randomly chosen or derived from some heuristic. In each iteration, the algorithm associates each data object with its nearest center, based on some chosen distance metric. The new centroids are calculated by taking the mean of all the data objects within each sub-cluster respectively. The algorithm iterates until no data objects move from one sub-cluster to another.

Our GPU implementation is based on the k-means implementation of Northwestern University's Minebench The source code of serial/OpenMP version of k-means which is developed by Minebench team can be downloaded from link1 and link2.

Our CUDA implementation can be downloaded from here. This version is used for our GPGPU workshop paper. A new version with more speedup and several optimizations will be released soon.