fanny: FuzzyKMeans Clustering.

In Fuzzy-Logix/AdapteR: Converts R syntax to SQL to provide fast analytics using in-database C++ library DB Lytix.

Description

fanny performs fuzzy analysis on FLTable objects.

Usage

fanny(x, k, ...)

Arguments

x	an object of class FLTable, can be wide or deep table
k	the number of clusters. t is required that 0 < k < n/2 where n is the number of observations.
diss	logical TRUE if x is dissimilarity matrix. currently not used.
memb.exp	degree of fuzziness or membership coefficient
metric	only "euclidean" distance supported currently
Stand	logical indicating if standardization should be done before calculating diss matrix
iniMem.p	inital membership matrix. Currently not used
cluster.only	logical if only clustering vector is needed as output
keep.diss	logicals indicating if the dissimilarities and/or input data x should be kept in the result
keep.data	logicals indicating if the dissimilarities and/or input data x should be kept in the result
maxit	maximum number of iterations
tol	tolerance used for convergence. Currently 0.000001
trace.lev	integer specifying a trace level for printing diagnostics during the build and swap phase of the algorithm. currently always 0
excludeCols	the comma separated character string of columns to be excluded
classSpec	list describing the categorical dummy variables
whereconditions	takes the where_clause as a string
distTable	name of the in-database table having dissimilarity matrix or distance table

Details

The DB Lytix function called is FLFKMeans.Fuzzy K-Means clusters the training data. The relationship of observations to clusters are weighted by the membership matrix, which is controlled by the degree of fuzziness.

Value

fanny returns a list and replicates equivalent R output from fanny in cluster package.The mapping table can be viewed using object$mapping if input is wide table.

Constraints

Plotting for large datasets takes longer time to fetch data. If classSpec is not specified, the categorical variables are excluded from analysis by default.

See Also

fanny for R function reference implementation.

Examples

widetable  <- FLTable(getTestTableName("iris"), 
					"obsid")
fannyobject <- fanny(widetable,2,memb.exp=2)
fannyobject$clustering
fannyobject$coeff
print(fannyobject)
plot(fannyobject)

##One can specify ClassSpec and transform categorical variables 
##before clustering. This increases the number of variables in the plot
##because categorical variable is split into binary numerical variables.

##The clusters may not be well-defined as is observed in the case below:-
widetable  <- FLTable(getTestTableName("iris"), "obsid")
fannyobjectnew <- fanny(widetable,3,classSpec=list("Species(setosa)"))
plot(fannyobjectnew)

Fuzzy-Logix/AdapteR documentation built on May 6, 2019, 5:07 p.m.