I know this is late but I just saw it today......
Last year it was suggested to me to use hierarchical clustering to derive
initial centers for a kmeans application. Basically, given a large dataset
with at least interval (better if ratio) measurements it makes indeed sense
1) Select randomly a sample of a few hundred cases
2) Run a hierarchical clustering procedure and save the centers generated
3) Feed into a kmeans procedure such centers, so that the final solution
applied to the entire dataset is less influenced by the arbitrary selection
of the initial centers
On http://www.listserv.uga.edu/cgi-bin/wa?A2=ind0404&L=spssx-l&P=R4505 you
shall find a reply I have got from Wim Beyers about my intention to use SPSS
twostep clustering method and some references to the above mentioned
Mr. Luca MEYER
Survey research, data analysis & more: http://www.lucameyer.com/
Tel: +390122854456 - Fax: +390122854837 - Mobile: + 393394950021
"If you can't feed a hundred people, then feed just one." - Mother Teresa -
> -----Messaggio originale-----
> Da: Classification, clustering, and phylogeny estimation
> [mailto:[log in to unmask]] Per conto di jessie jessie
> Inviato: venerd́ 9 settembre 2005 22.31
> A: [log in to unmask]
> Oggetto: Using more than one clustering method...
> If I use more than one clustering method, e.g. Kmeans
> and Hierarchical, to cluster the same set of data,
> does the joint operation necessarily increase my
> chance of finding the underlying clusters that might
> be verified in the future? Is there any books/papers
> that talk about this topic? Thanks a lot in advance!
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