fanny {cluster} R Documentation

## Fuzzy Analysis Clustering

### Description

Computes a fuzzy clustering of the data into `k` clusters.

### Usage

```fanny(x, k, diss = inherits(x, "dist"), memb.exp = 2,
metric = c("euclidean", "manhattan", "SqEuclidean"),
stand = FALSE, iniMem.p = NULL, cluster.only = FALSE,
keep.diss = !diss && !cluster.only && n < 100,
keep.data = !diss && !cluster.only,
maxit = 500, tol = 1e-15, trace.lev = 0)
```

### Arguments

 `x` data matrix or data frame, or dissimilarity matrix, depending on the value of the `diss` argument. In case of a matrix or data frame, each row corresponds to an observation, and each column corresponds to a variable. All variables must be numeric. Missing values (NAs) are allowed. In case of a dissimilarity matrix, `x` is typically the output of `daisy` or `dist`. Also a vector of length n*(n-1)/2 is allowed (where n is the number of observations), and will be interpreted in the same way as the output of the above-mentioned functions. Missing values (NAs) are not allowed. `k` integer giving the desired number of clusters. It is required that 0 < k < n/2 where n is the number of observations. `diss` logical flag: if TRUE (default for `dist` or `dissimilarity` objects), then `x` is assumed to be a dissimilarity matrix. If FALSE, then `x` is treated as a matrix of observations by variables. `memb.exp` number r strictly larger than 1 specifying the membership exponent used in the fit criterion; see the ‘Details’ below. Default: `2` which used to be hardwired inside FANNY. `metric` character string specifying the metric to be used for calculating dissimilarities between observations. Options are `"euclidean"` (default), `"manhattan"`, and `"SqEuclidean"`. Euclidean distances are root sum-of-squares of differences, and manhattan distances are the sum of absolute differences, and `"SqEuclidean"`, the squared euclidean distances are sum-of-squares of differences. Using this last option is equivalent (but somewhat slower) to computing so called “fuzzy C-means”. If `x` is already a dissimilarity matrix, then this argument will be ignored. `stand` logical; if true, the measurements in `x` are standardized before calculating the dissimilarities. Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the variable's mean absolute deviation. If `x` is already a dissimilarity matrix, then this argument will be ignored. `iniMem.p` numeric n x k matrix or `NULL` (by default); can be used to specify a starting `membership` matrix, i.e., a matrix of non-negative numbers, each row summing to one.
 `cluster.only` logical; if true, no silhouette information will be computed and returned, see details.
 `keep.diss, keep.data` logicals indicating if the dissimilarities and/or input data `x` should be kept in the result. Setting these to `FALSE` can give smaller results and hence also save memory allocation time. `maxit, tol` maximal number of iterations and default tolerance for convergence (relative convergence of the fit criterion) for the FANNY algorithm. The defaults `maxit = 500` and ```tol = 1e-15``` used to be hardwired inside the algorithm. `trace.lev` integer specifying a trace level for printing diagnostics during the C-internal algorithm. Default `0` does not print anything; higher values print increasingly more.

### Details

In a fuzzy clustering, each observation is “spread out” over the various clusters. Denote by u(i,v) the membership of observation i to cluster v.

The memberships are nonnegative, and for a fixed observation i they sum to 1. The particular method `fanny` stems from chapter 4 of Kaufman and Rousseeuw (1990) (see the references in `daisy`) and has been extended by Martin Maechler to allow user specified `memb.exp`, `iniMem.p`, `maxit`, `tol`, etc.

Fanny aims to minimize the objective function

SUM_[v=1..k] (SUM_(i,j) u(i,v)^r u(j,v)^r d(i,j)) / (2 SUM_j u(j,v)^r)

where n is the number of observations, k is the number of clusters, r is the membership exponent `memb.exp` and d(i,j) is the dissimilarity between observations i and j.
Note that r -> 1 gives increasingly crisper clusterings whereas r -> Inf leads to complete fuzzyness. K&R(1990), p.191 note that values too close to 1 can lead to slow convergence. Further note that even the default, r = 2 can lead to complete fuzzyness, i.e., memberships u(i,v) == 1/k. In that case a warning is signalled and the user is advised to chose a smaller `memb.exp` (=r).

Compared to other fuzzy clustering methods, `fanny` has the following features: (a) it also accepts a dissimilarity matrix; (b) it is more robust to the `spherical cluster` assumption; (c) it provides a novel graphical display, the silhouette plot (see `plot.partition`).

### Value

an object of class `"fanny"` representing the clustering. See `fanny.object` for details.

`agnes` for background and references; `fanny.object`, `partition.object`, `plot.partition`, `daisy`, `dist`.

### Examples

```## generate 10+15 objects in two clusters, plus 3 objects lying
## between those clusters.
x <- rbind(cbind(rnorm(10, 0, 0.5), rnorm(10, 0, 0.5)),
cbind(rnorm(15, 5, 0.5), rnorm(15, 5, 0.5)),
cbind(rnorm( 3,3.2,0.5), rnorm( 3,3.2,0.5)))
fannyx <- fanny(x, 2)
## Note that observations 26:28 are "fuzzy" (closer to # 2):
fannyx
summary(fannyx)
plot(fannyx)

(fan.x.15 <- fanny(x, 2, memb.exp = 1.5)) # 'crispier' for obs. 26:28
(fanny(x, 2, memb.exp = 3))               # more fuzzy in general

data(ruspini)
f4 <- fanny(ruspini, 4)
stopifnot(rle(f4\$clustering)\$lengths == c(20,23,17,15))
plot(f4, which = 1)
## Plot similar to Figure 6 in Stryuf et al (1996)
plot(fanny(ruspini, 5))
```

[Package cluster version 2.1.0 Index]