This paper proposes a fast k-means algorithm for graphs based on Elkan's k-means for vectors. To accelerate the k-means algorithm for graphs without trading computational time against solution quality, we avoid unnecessary graph distance calculations by exploiting the triangle inequality of the underlying distance metric. In experiments we show that the accelerated k-means for graphs is faster than k-means for graphs provided there is a cluster structure in the data. © 2010 Springer-Verlag.
CITATION STYLE
Jain, B. J., & Obermayer, K. (2010). Elkan’s k-means algorithm for graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6438 LNAI, pp. 22–32). https://doi.org/10.1007/978-3-642-16773-7_2
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