increaseB
Increase reference data sets
Description
returns a gap criterion clustering evaluation object updatedEvaluation
= increaseB(evaluation
,numsets
)updatedEvaluation
,
which uses the gap criterion clustering evaluation object evaluation
and an additional number of reference data sets specified by
numsets
.
Examples
Evaluate Clustering Solutions Using Additional Reference Data
Create a gap clustering evaluation object using evalclusters
. Then, use increaseB
to increase the number of reference data sets used to compute the gap criterion values.
Load the fisheriris
data set. The data contains length and width measurements from the sepals and petals of three species of iris flowers.
load fisheriris
Cluster the flower measurement data using kmeans
, and use the gap criterion to evaluate proposed solutions for 1 to 5 clusters. Use 50 reference data sets.
rng("default") % For reproducibility evaluation = evalclusters(meas,"kmeans","gap","KList",1:5,"B",50)
evaluation = GapEvaluation with properties: NumObservations: 150 InspectedK: [1 2 3 4 5] CriterionValues: [0.0870 0.5822 0.8766 1.0007 1.0465] OptimalK: 4
The clustering evaluation object evaluation
contains data on each proposed clustering solution. The returned results indicate that the optimal number of clusters is four.
The value of the B
property of evaluation
shows 50 reference data sets.
evaluation.B
ans = 50
Increase the number of reference data sets by 100, for a total of 150 sets.
evaluation = increaseB(evaluation,100)
evaluation = GapEvaluation with properties: NumObservations: 150 InspectedK: [1 2 3 4 5] CriterionValues: [0.0794 0.5850 0.8738 1.0034 1.0508] OptimalK: 5
The returned results now indicate that the optimal number of clusters is five.
The value of the B
property of evaluation
now shows 150 reference data sets.
evaluation.B
ans = 150
Input Arguments
evaluation
— Clustering evaluation data
GapEvaluation
object
Clustering evaluation data, specified as a GapEvaluation
clustering evaluation object. Create a clustering evaluation
object by using evalclusters
.
numsets
— Number of additional reference data sets
positive integer scalar
Number of additional reference data sets, specified as a positive integer scalar.
Data Types: single
| double
Output Arguments
updatedEvaluation
— Updated clustering evaluation data
GapEvaluation
object
Updated clustering evaluation data, returned as a GapEvaluation
clustering evaluation object.
updatedEvaluation
contains evaluation data obtained using the
reference data sets from the evaluation
object and a number of
additional reference data sets specified by numsets
.
The increaseB
function updates the B
property of the evaluation
object to reflect the increase in the
number of reference data sets used to compute the gap criterion values. The function
also updates the CriterionValues
property with gap criterion values
computed using the total number of reference data sets. If the software finds a new
optimal number of clusters and optimal clustering solution when using the total number
of reference data sets, then increaseB
updates the
OptimalK
and OptimalY
properties. The function
also updates the LogW
, ExpectedLogW
,
StdLogW
, and SE
properties.
Version History
Introduced in R2014a
See Also
MATLAB 명령
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