Introduction

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.

Publications

  1. Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming.
    Cite this
    Wang H, Song M, 2011-12-01 - The R journal

Credits

  1. Haizhou Wang
    Developer

    Department of Computer Science, New Mexico State University, United States of America

  2. Mingzhou Song
    Investigator

    Department of Computer Science, New Mexico State University, United States of America

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Summary
AccessionBT003250
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesC++, R
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByMingzhou Song