Message-ID: <5F1C1BC5-1C82-4239-85A1-D40199BB16A8@yahoo.es>
Date: 2016-04-12T21:37:20Z
From: Luisfo
Subject: Dissimilarity matrix and number clusters determination
In-Reply-To: <CA+pG8eNVyCDyZ5-zyjG=Bqyqb2=J8Zu7XtLBeBuBhKB_KoE2Bw@mail.gmail.com>
Dear Michael,
Yes, AFAIK you are correctly reading the results.
You can print
elbow.obj$k
to obtain the optimal number of clusters, and ?visually? you can check it plotting the variance vs #clusters
plot(css.obj$k, css.obj$ev)
HTH
Best,
Luisfo Chiroque
PhD Student
IMDEA Networks Institute
http://fourier.networks.imdea.org/people/~luis_nunez/ <http://fourier.networks.imdea.org/people/~luis_nunez/>
> El 12 abr 2016, a las 4:30, Michael Artz <michaeleartz at gmail.com> escribi?:
>
> Hi,
> I already have a dissimilarity matrix and I am submitting the results to
> the elbow.obj method to get an optimal number of clusters. Am I reading
> the below output correctly that I should have 17 clusters?
>
> code:
> top150 <- sampleset[1:150,]
> {cluster1 <- daisy(top150
> , metric = c("gower")
> , stand = TRUE
> , type = list(symm = 1))
> }
>
> dist.obj <- dist(cluster1)
> hclust.obj <- hclust(dist.obj)
> css.obj <- css.hclust(dist.obj,hclust.obj)
> elbow.obj <- elbow.batch(css.obj)
>
> [1] "A \"good\" k=17 (EV=0.80) is detected when the EV is no less than
> 0.8\nand the increment of EV is no more than 0.01 for a bigger k.\n"
> attr(,"class")
>
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>
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