Description Usage Arguments Details Value Constraints See Also Examples
fanny
performs fuzzy analysis on FLTable objects.
1 |
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 |
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 |
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.
fanny
returns a list and replicates equivalent R output
from fanny
in cluster package.The mapping table can be viewed
using object$mapping
if input is wide table.
Plotting for large datasets takes longer time to fetch data. If classSpec is not specified, the categorical variables are excluded from analysis by default.
fanny
for R function reference implementation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | widetable <- FLTable(getTestTableName("iris"),
"obsid")
fannyobject <- fanny(widetable,2,memb.exp=2)
fannyobject$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)
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