Description Usage Arguments Details Value References Examples

The SUP is a distance-based method for clustering. The idea of this algorithm is similar to gravitational attraction: every sample gravitates towards one another. The algorithm mimics the process of gravitational attraction iteratively that eventually merges the samples into clusters on the sample space. During the iterations, all samples continue moving until the system becomes stable.

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`x` |
data matrix. Each row is an instance of the data. |

`r` |
numeric vector or |

`rp` |
numeric vector or |

`t` |
either numeric vector, list of function, or one of |

`tolerance` |
numeric value. The threshold of convergence. |

`cluster.tolerance` |
numeric value. After iterations, if the distance of two points are smaller than |

`drop` |
logical value. Whether to delete the list structure if its length is 1. |

`implementation` |
eithor |

`sort` |
logical value. Whether to sort the cluster id by size. |

`verbose` |
logical value. Whether to show the iteration history. |

Please check the vignettes via `vignette("supc", package = "supc")`

for details.

`supc1`

returns a list of objects of class "supc".

Each "supc" object contains the following elements:

`x` |
The input matrix. |

`d0` |
The pairwise distance matrix of |

`r` |
The value of |

`t` |
The function |

`cluster` |
The cluster id of each instance. |

`centers` |
The center of each cluster. |

`size` |
The size of each cluster. |

`iteration` |
The number of iterations before convergence. |

`result` |
The position of data after iterations. |

Shiu, Shang-Ying, and Ting-Li Chen. 2016. "On the Strengths of the Self-Updating Process Clustering Algorithm." Journal of Statistical Computation and Simulation 86 (5): 1010–1031. doi: 10.1080/00949655.2015.1049605.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
set.seed(1)
X <- local({
mu <- list(
x = c(0, 2, 1, 6, 8, 7, 3, 5, 4),
y = c(0, 0, 1, 0, 0, 1, 3, 3, 4)
)
X <- lapply(1:5, function(i) {
cbind(rnorm(9, mu$x, 1/5), rnorm(9, mu$y, 1/5))
})
X <- do.call(rbind, X)
n <- nrow(X)
X <- rbind(X, matrix(0, 20, 2))
k <- 1
while(k <= 20) {
tmp <- c(13*runif(1)-2.5, 8*runif(1)-2.5)
y1 <- mu$x - tmp[1]
y2 <- mu$y - tmp[2]
y <- sqrt(y1^2+y2^2)
if (min(y)> 2){
X[k+n,] <- tmp
k <- k+1
}
}
X
})
X.supcs <- supc1(X, r = c(0.9, 1.7, 2.5), t = "dynamic", implementation = "R")
X.supcs$cluster
plot(X.supcs[[1]], type = "heatmap", major.size = 2)
plot(X.supcs[[2]], type = "heatmap", col = cm.colors(24), major.size = 5)
X.supcs <- supc1(X, r = c(1.7, 2.5), t = list(
function(t) {1.7 / 20 + exp(t) * (1.7 / 50)},
function(t) {exp(t)}
), implementation = "R")
plot(X.supcs[[1]], type = "heatmap", major.size = 2)
plot(X.supcs[[2]], type = "heatmap", col = cm.colors(24), major.size = 5)
``` |

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