Dear list members, I have one appeal for you.
I need use ROCK (RockCluster) algorithm for binary data in R. My binary data looks this: |objects cat1 cat2 cat3 cat4 ...A TRUE FALSE FALSE FALSE B TRUE FALSE TRUE FALSE C TRUE FALSE FALSE FALSE D FALSE TRUE TRUE TRUE E TRUE TRUE TRUE TRUE F TRUE FALSE TRUE FALSE| Now I need clasify these objects A-F to clusters. I apply this procedure https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/RockCluster#Dataset But I have several problems. 1. I import data from CSV file. |db <- read.csv(file="file.csv", header=TRUE, sep="|")| Fields are 1 (TRUE) and 0 (FALSE). 2. I convert this data: |x <- as.dummy(db[-1]|). After this step all columns in x are duplicated with 1 and 0. Why? It is correct please? 3. |rc <- rockCluster(x, n=4, debug=TRUE)| 4. |rf <- fitted(rc)| Why |fitted| and when rather use |predict(rc, x)|? 5. |table(db$objects, rf$cl)| After I get this output: | 1 NA A 1 0 B 1 0 C 1 0 D 0 1 E 0 1 F 0 1 | What way I can read this output? What objects are in clusters with other? What objects are the most similar please? Many thanks for your help. -- Best Regards Matej Zuzcak [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
