Selection of n in K-Means Algorithm

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Provided by: Research In Motion
Topic: Big Data
Format: PDF
One of the most popular and widely used algorithms in data clustering is K-means algorithm. However, one of its main downsides is that user has to specify number of clusters that is n, before the algorithm is to be implemented anywhere. This paper reviews existing methods for choosing the available number of clusters for the algorithm as well as the factors which are affecting the selection and proposed measure in the end of the paper which assist the selection.
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