Finding the k in K-means Clustering: A Comparative Analysis Approach

AI 2015: ADVANCES IN ARTIFICIAL INTELLIGENCE, pp. 356-364, 2015.

Cited by: 0|Bibtex|Views5|DOI:https://doi.org/10.1007/978-3-319-26350-2_31
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Abstract:

This paper explores the application of inequality indices, a concept successfully applied in comparative software analysis among many application domains, to find the optimal value k for k-means when clustering road traffic data. We demonstrate that traditional methods for identifying the optimal value for k (such as gap statistic and Pha...More

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