On K-Means Clustering Using Mahalanobis Distance
A problem that arises quite frequently in statistics is that of identifying groups, or clusters, of data within a population or sample. The most widely used procedure to identify clusters in a set of observations is known as K-Means. The main limitation of this algorithm is that it uses the Euclidea...
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| Format: | Others |
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North Dakota State University
2017
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| Online Access: | https://hdl.handle.net/10365/26766 |