Description Usage Arguments Details Value Constraints See Also Examples
agnes
computes agglomeraive hierarchial
clustering on FLTable objects.
1 |
x |
an object of class FLTable, can be wide or deep table |
diss |
logical if |
metric |
only "euclidean" distance supported currently |
Stand |
logical indicating if standardization should be done before calculating diss matrix |
method |
character. Allowed methods are "average", "single", "complete", "centroid" |
par.method |
currently not used and always 0 |
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 |
trace.lev |
integer specifying a trace level for printing diagnostics during the build and swap phase of the algorithm. currently always 0 |
maxit |
maximum number of iterations |
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 FLAggClustering. In the initialization, each observation in the dataset belongs to its own cluster. In each iteration, agglomerative clustering would aggregate the two clusters that are nearest to each other, for which the distance is measured by the linkage method. This would continue until either the entire dataset belongs to one cluster or until the maximum number of iterations has been reached
agnes
returns a list and replicates equivalent R output
from agnes
in cluster package. The mapping table can be viewed
using mapping
component, if input is wide table.
Plotting for large datasets takes longer time to fetch data. Error is thrown if results cannot be fetched. maxit should be more than no.of. observations for algorithm to reach completion. Error is thrown if algorithm does not reach completion or more than one cluster is formed at any step. If classSpec is not specified, the categorical variables are excluded from analysis by default.
agnes
for R reference implementation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | deeptable <- FLTable("tblUSArrests", "ObsID","VarID","Num_Val")
agnesobject <- agnes(deeptable,maxit=50)
print(agnesobject)
plot(agnesobject)
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( "iris", "obsid")
agnesobjectnew <- agnes(widetable,maxit=500,classSpec=list("Species(setosa)"))
The below plot throws warnings!
plot(agnesobjectnew)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.