cshell {e1071} | R Documentation |

The *c*-shell clustering algorithm, the shell prototype-based version
(ring prototypes) of the fuzzy *k*means clustering method.

cshell(x, centers, iter.max=100, verbose=FALSE, dist="euclidean", method="cshell", m=2, radius = NULL)

`x` |
The data matrix, were columns correspond to the variables and rows to observations. |

`centers` |
Number of clusters or initial values for cluster centers |

`iter.max` |
Maximum number of iterations |

`verbose` |
If `TRUE` , make some output during learning |

`dist` |
Must be one of the following: If `"euclidean"` , the
mean square error, if `"manhattan"` , the mean absolute error is
computed. Abbreviations are also accepted. |

`method` |
Currently, only the `"cshell"` method; the c-shell fuzzy
clustering method |

`m` |
The degree of fuzzification. It is defined for values greater
than 1 |

`radius` |
The radius of resulting clusters |

The data given by `x`

is clustered by the fuzzy *c*-shell algorithm.

If `centers`

is a matrix, its rows are taken as the initial cluster
centers. If `centers`

is an integer, `centers`

rows
of `x`

are randomly chosen as initial values.

The algorithm stops when the maximum number of iterations (given by
`iter.max`

) is reached.

If `verbose`

is `TRUE`

, it displays for each iteration the number
the value of the objective function.

If `dist`

is `"euclidean"`

, the distance between the
cluster center and the data points is the Euclidean distance (ordinary
kmeans algorithm). If `"manhattan"`

, the distance between the
cluster center and the data points is the sum of the absolute values
of the distances of the coordinates.

If `method`

is `"cshell"`

, then we have the *c*-shell
fuzzy clustering method.

The parameters `m`

defines the degree of fuzzification. It is
defined for real values greater than 1 and the bigger it is the more
fuzzy the membership values of the clustered data points are.

The parameter `radius`

is by default set to *0.2* for every
cluster.

`cshell`

returns an object of class `"cshell"`

.

`centers` |
The final cluster centers. |

`size` |
The number of data points in each cluster. |

`cluster` |
Vector containing the indices of the clusters where the data points are assigned to. The maximum membership value of a point is considered for partitioning it to a cluster. |

`iter` |
The number of iterations performed. |

`membership` |
a matrix with the membership values of the data points to the clusters. |

`withinerror` |
Returns the sum of square distances within the clusters. |

`call` |
Returns a call in which all of the arguments are specified by their names. |

Evgenia Dimitriadou

Rajesh N. Dave. *Fuzzy Shell-Clustering and Applications to Circle
Detection in Digital Images.* Int. J. of General Systems, Vol. **16**,
pp. 343-355, 1996.

## a 2-dimensional example x<-rbind(matrix(rnorm(50,sd=0.3),ncol=2), matrix(rnorm(50,mean=1,sd=0.3),ncol=2)) cl<-cshell(x,2,20,verbose=TRUE,method="cshell",m=2) print(cl) # assign classes to some new data y<-rbind(matrix(rnorm(13,sd=0.3),ncol=2), matrix(rnorm(13,mean=1,sd=0.3),ncol=2)) # ycl<-predict(cl, y, type="both")

[Package *e1071* version 1.5-2 Index]