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HI R users With clara function I get a data frame (maybe this is not the exact word, I'm new to R) with the following variables: > names(myclara) [1] "sample" "medoids" "i.med" "clustering" "objective" [6] "clusinfo" "diss" "call" "silinfo" "data" I want to export "clustering" and "data" to a new text file so I try > write.table(myclara$data,"cluster.dat") > write.table(myclara$clustering,"cluster.dat"...

...ex=2) Now, i would like to do *the same process for 5 times*, so that i can view the clusters in data have been moving and the center changes with various results. (Animation-like) My main concern here, is how to put this in a *loop form* or any other methods are available? I tried with:* clarax$clusinfo I thought of doing it to activate Device (1) & (2)* ** *Really need anyones help!* ** *JJ* *---* -- Lecturer J. Joshua Thomas KDU College Penang Campus Research Student, University Sains Malaysia [[alternative HTML version deleted]]

Dear All , For more process on data resulting clustering, one needs to access data within any cluster. How it is possible in CLARA? $clustering :gives us clustering vector as mixed, so it isn't as separate $clusinfo : gives us dissimilarities, Thanks a lot Amir --------------------------------- [[alternative HTML version deleted]]

...P.S.: For example "clara": Medoids: Objective function: Clustering vector: Cluster sizes: Best sample: Available components: [1] "sample" "medoids" "i.med" "clustering" "objective" "clusinfo" "diss" [8] "call" "silinfo" "data" But "kmeans" has: K-means clustering with 2 clusters of sizes 50, 50 Cluster means: Clustering vector: Within cluster sum of squares by cluster: Available components: [1] &qu...

...ve less than 'x' obs. So I'm fiddling with dendrogram objects. Is there any way of using the 'members' attribute to cut a dendrogram to only include branches with more than 'x' members? An example output from clara with a data set of 1000 obs. and 82 clusters > cl$clusinfo size max_diss av_diss isolation [1,] 1 0.00000000 0.00000000 0.0000000 [2,] 3 1.19840221 0.40837142 5.0938561 [3,] 4 0.16867940 0.07284916 0.5830662 [4,] 2 0.13380551 0.06690276 0.5687456 [5,] 3 0.21862177 0.13428115 1.0371933 [6,] 5 0.10384573 0.05270335 0.588788...

...ds: ID [1,] 5 5 [2,] 9 9 Clustering vector: [1] 1 1 1 1 1 2 1 2 2 2 1 1 Objective function: build swap 0.01873272 0.01620992 Available components: [1] "medoids" "id.med" "clustering" "objective" "isolation" [6] "clusinfo" "silinfo" "diss" "call" > ndex.G1(as.matrix(alpha.vec),cl$cluster,d=dist.mat,centrotypes="medoids") Error: could not find function "ndex.G1" > index.G1(as.matrix(alpha.vec),cl$cluster,d=dist.mat,centrotypes="medoids...