Description Usage Arguments Details Value Author(s) References Examples

View source: R/MultiChannel.WUC.R

Perform optimal multi-channel weighted univariate *k*-means clustering in linear time.

1 | ```
MultiChannel.WUC(x, y, k=c(1,9))
``` |

`x` |
a numeric vector of data to be clustered. All |

`y` |
a numeric matrix of non-negative weights for each element in |

`k` |
either an exact integer number of clusters, or a vector of length two specifying the minimum and maximum numbers of clusters to be examined. The default is |

`MultiChannel.WUC`

minimizes the total weighted within-cluster sum of squared distance \insertCitezhong2019modelfreeCkmeans.1d.dp. It uses the SMAWK algorithm \insertCiteaggarwal1987geometricCkmeans.1d.dp with modified data structure to speed up the dynamic programming to linear runtime. The method selects an optimal `k`

based on an approximate Gaussian mixture model using the BIC.

A list object containing the following components:

`cluster` |
a vector of clusters assigned to each element in |

`centers` |
a numeric vector of the (weighted) means for each cluster. |

`withinss` |
a numeric vector of the (weighted) within-cluster sum of squares for each cluster. |

`size` |
a vector of the (weighted) number of elements in each cluster. |

`totss` |
total sum of (weighted) squared distances between each element and the sample mean. This statistic is not dependent on the clustering result. |

`tot.withinss` |
total sum of (weighted) within-cluster squared distances between each element and its cluster mean. This statistic is minimized given the number of clusters. |

`betweenss` |
sum of (weighted) squared distances between each cluster mean and sample mean. This statistic is maximized given the number of clusters. |

`xname` |
a character string. The actual name of the |

`yname` |
a character string. The actual name of the |

Hua Zhong and Mingzhou Song

1 2 3 4 |

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