K-median problem: Difference between revisions
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The '''''k''-median problem''' is the problem of finding ''k'' centers such that the clusters formed by them are the most compact. |
The '''''k''-median problem''' is the problem of finding ''k'' centers such that the clusters formed by them are the most compact. |
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Formally, given a set of data points ''x'', the ''k'' centers ''c''<sub>''i''</sub> are to be chosen so as to minimize the sum of the |
Formally, given a set of data points ''x'', the ''k'' centers ''c''<sub>''i''</sub> are to be chosen so as to minimize the sum of the absolute values of the distances from each ''x'' to the nearest ''c''<sub>''i''</sub>. |
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The problem constitutes a better measure for the [[k-means clustering|''k''-means clustering]] algorithm, and is widely used in applications such as [[facility location]]<ref>http://www.aladdin.cs.cmu.edu/reu/mini_probes/papers/facilitylocation.ppt</ref>. |
The problem constitutes a better measure for the [[k-means clustering|''k''-means clustering]] algorithm, and is widely used in applications such as [[facility location]]<ref>http://www.aladdin.cs.cmu.edu/reu/mini_probes/papers/facilitylocation.ppt</ref>. |
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Revision as of 13:15, 22 April 2010
The k-median problem is the problem of finding k centers such that the clusters formed by them are the most compact.
Formally, given a set of data points x, the k centers ci are to be chosen so as to minimize the sum of the absolute values of the distances from each x to the nearest ci.
The problem constitutes a better measure for the k-means clustering algorithm, and is widely used in applications such as facility location[1].
References
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