Turning Big Data Into Small Data: Hardware Aware Approximate Clustering With Randomized SVD and Coresets
Organizing data into groups using unsupervised learning algorithms such as k-means clustering and GMM are some of the most widely used techniques in data exploration and data mining. As these clustering algorithms are iterative by nature, for big datasets it is increasingly challenging to find clust...
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| Format: | Others |
| Language: | en |
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Harvard University
2015
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| Online Access: | http://nrs.harvard.edu/urn-3:HUL.InstRepos:14398541 |