A fast dynamic programming algorithm for optimal univariate clustering. Minimizing the sum of squares of within-cluster distances, the algorithm guarantees optimality and reproducibility. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. With optional weights, the algorithm can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful alternative to heuristic methods for univariate data analysis.
|Author||Joe Song [aut, cre], Haizhou Wang [aut]|
|Date of publication||2017-02-16 17:57:06|
|Maintainer||Joe Song <firstname.lastname@example.org>|
|License||LGPL (>= 3)|
ahist: Adaptive Histograms
Ckmeans.1d.dp: Optimal and Fast Univariate Clustering
Ckmeans.1d.dp-package: Optimal and Fast Univariate Clustering
plotBIC: Plot Bayesian Information Criterion as a Function of Number...
plot.Ckmeans.1d.dp: Plot Optimal Univariate Clustering Results
print.Ckmeans.1d.dp: Print Optimal Univariate Clustering Results