help clfuzz

Title

clfuzz -- Fuzzy clustering from distance matrix

Syntax

clfuzz newvarname, DISTmat(string) IDvar(varname) K(real) [FUZZiness(real 3.0)]

options

Required
newvarname a single new variable in which to store the strongest cluster membership

distmat(matname) names the distance matrix

idvar(varname) identifies the variable that links the sort-order of the distance matrix to the sort-order of the data

k The number of clusters

fuzziness(real) Fuzziness coefficient (defaults to 3.0).

Description

clfuzz carries out Fuzzy C-medoids clustering, from the
pairwise distance matrix.

Fuzzy C-medoids clustering is a non-hierarchical approach to
clustering data.

Minimises the sum of weighted distances to each cluster medoid,
where the weight is based on the object's attachment to the cluster.

Returns a variable holding the strongest cluster membership and an
N by k matrix of object-cluster attachment strengths.

Note: this is an experimental implementation. Feedback is welcome.

Remarks

Because the order of the data and the order of the distance matrix
must coincide, the dataset must be sorted by idvar. It is the
user's responsibility that this variable defines the correct
order.

Returns

r(V): k by 1 matrix of index numbers of medoids

r(uij): _N by k matrix of attachments

References

Bezdek, J. (1981). Pattern Recognition with Fuzzy Objective
Function Algorithms. Plenum Press, New York.

Krishnapuram, R., Joshi, A., and Yi, L. (1999). A fuzzy
relative of the k-medoids algorithm with application to web document and
snippet clustering. In Fuzzy Systems Conference Proceedings, 1999.
FUZZ-IEEE '99. 1999 IEEE International, volume 3, pages 1281-1286 vol.3.

Author

Brendan Halpin, brendan.halpin@ul.ie

Examples

. clfuzz f4, dist(distances) id(id) k(4)

See Also

clpam

getmedoids

calinski

dudahart

discrepancy

SADI