Description Usage Arguments Details Value Author(s) See Also Examples

This function creates a set of fuzzy attributes from crisp data. Factors, numeric vectors,
matrix or data frame columns are transformed into a set of fuzzy attributes, i.e. columns with
membership degrees. Unlike `lcut`

, for transformation is not used the linguistic
linguistic approach, but partitioning using regular shapes of the fuzzy sets (such as triangle,
raised cosine).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |

`x` |
Data to be transformed: a vector, matrix, or data frame. Non-numeric data are allowed. |

`breaks` |
This argument determines the break-points of the positions of the fuzzy sets. It should be
an ordered vector of numbers such that the I.e. the minimum number of breaks-points is 3; If considering an i-th fuzzy set
(of The resulting fuzzy sets would be named after the original data by adding dot (".") and a
number Unlike For non-numeric data, this argument is ignored. For |

`name` |
A name to be added as a suffix to the created fuzzy attribute names. This parameter
can be used only if |

`type` |
The type of fuzzy sets to create Currently, |

`merge` |
This argument determines whether to derive additional fuzzy sets by merging the elementary
fuzzy sets (whose position is determined with the
The names of the derived (merged) fuzzy sets is derived from the names of the original elementary fuzzy sets by concatenating them with the "|" (pipe) separator. |

`parallel` |
Whether the processing should be run in parallel or not. Parallelization is
implemented using the |

`...` |
Other parameters to some methods. |

The aim of this function is to transform numeric data into a set of fuzzy attributes. The result is in the form of the object of class "fsets", i.e. a numeric matrix whose columns represent fuzzy sets (fuzzy attributes) with values being the membership degrees.

The function behaves diffently to the type of input `x`

.

If `x`

is a factor or a logical vector (or other non-numeric data) then for each distinct
value of an input, a fuzzy set is created, and data would be transformed into crisp membership
degrees 0 or 1 only.

If `x`

is a numeric vector then fuzzy sets are created accordingly to break-points
specified in the `breaks`

argument with 1st, 2nd and 3rd break-point specifying the first
fuzzy set, 2nd, 3rd and 4th break-point specifying th second fuzzy set etc. The shape of the
fuzzy set is determined by the `type`

argument that may be equal either to a string
`'triangle'`

or `'raisedcos'`

or it could be a function that computes the membership
degrees for itself (see `triangle`

or `raisedcos`

functions for
details). Additionally, super-sets of these elementary sets may be created by specifying the
`merge`

argument. Values of this argument specify how many consecutive fuzzy sets should be
combined (by using the Lukasiewic's t-conorm) to produce super-sets - see the description of
`merge`

above.

If a matrix (resp. data frame) is provided to this function instead of single vector, all columns are
processed separately as described above and the result is combined with the
`cbind.fsets`

function.

The function sets up properly the `vars`

and `specs`

properties of
the result.

An object of class "fsets" is returned, which is a numeric matrix with columns representing the
fuzzy attributes. Each source columm
of the `x`

argument corresponds to multiple columns in the resulting matrix.
Columns have names that indicate the name of the source as well as a index *i* of fuzzy set(s)
– see the description of arguments `breaks`

and `merge`

above.

The resulting object would also have set the `vars`

and `specs`

properties with the former being created from original column names (if `x`

is a matrix or
data frame) or the `name`

argument (if `x`

is a numeric vector). The
`specs`

incidency matrix would be created to reflect the superset-hood of the merged
fuzzy sets.

Michal Burda

`lcut`

,
`farules`

,
`pbld`

`vars`

,
`specs`

,
`cbind.fsets`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# fcut on non-numeric data
ff <- factor(substring("statistics", 1:10, 1:10), levels = letters)
fcut(ff)
# transform a single vector into a single fuzzy set
x <- runif(10)
fcut(x, breaks=c(0, 0.5, 1), name='age')
# transform single vector into a partition of the interval 0-1
# (the boundary triangles are right-angled)
fcut(x, breaks=c(0, 0, 0.5, 1, 1), name='age')
# also create supersets
fcut(x, breaks=c(0, 0, 0.5, 1, 1), name='age', merge=c(1, 2))
# transform all columns of a data frame
# with different breakpoints
data <- CO2[, c('conc', 'uptake')]
fcut(data, breaks=list(conc=c(95, 95, 350, 1000, 1000),
uptake=c(7, 7, 28.3, 46, 46)))
``` |

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