Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming

By Haizhou Wang, Mingzhou Song. The R Journal, 2011

391 citations recorded by the source corpus.

The heuristic k-means algorithm, widely used for cluster analysis, does not guarantee optimality. We developed a dynamic programming algorithm for optimal one-dimensional clustering. The algorithm is implemented as an R package called Ckmeans.1d.dp. We demonstrate its advantage in optimality and runtime over the standard iterative k-means algorithm.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d76408a46d84fb9f028edac6c103bd735c143f05

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Robin I. M. Dunbar
2. Andrea Passarella
3. Marco Conti
4. Valerio Arnaboldi

Citations in the corpus

• The structure of online social networks mirrors those in the offline world (2015, methodology): “For each ego network, we order alters in a one dimensional space by contact frequency with the ego, and search for clusters in this one dimensional space using the technique described in Wang & Song (2011).”