Ckmeans.1d.dp: Optimal, Fast, and Reproducible Univariate Clustering
Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) <doi:10.32614/RJ-2011-015> (Song & Zhong 2020) <doi:10.1093/bioinformatics/btaa613>, k-median, k-segments, and multi-channel weighted k-means. Dynamic programming is used to minimize the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced when there are many clusters. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility, useful for peak calling on temporal, spatial, and spectral data.
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Reverse dependencies:
| Reverse depends: | GenomicOZone |
| Reverse imports: | autostats,CellBarcode,clusterHD,GridOnClusters,Harman,kcmeans,OptCirClust,SILFS,SPECK,STREAK,weitrix |
| Reverse suggests: | bakR,CytoProfile,DiffXTables,FunChisq,mapsf,xgboost |
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[aut, cre], Hua Zhong