Hello Dan, many thanks for your reply. I have really 6 objects, I am sorry for my mistake in my previous mail. So I will try use ROCK algorithm for next data set and I will more study output yet.
-- Best Regards Matej Zuzcak Dňa 17.8.2016 o 1:58 Nordlund, Dan (DSHS/RDA) napísal(a): > You should really go to the help page for the function rockCluster() and run > the first example and study the output. It should become clear that what you > are calling the <NA> cluster is not a cluster at all. It is an indicator of > which objects *did not* cluster with any other objects ). > > In addition, you state you have only four objects. This is confusing since > you have a column in your data named 'objects' which implies that you have 6 > objects (and that is how many objects are in your cluster results). > > The function, fitted() should be used with the data you are clustering. If > you want to "predict" what clusters NEW data would fall into, then use > predict(). It is not surprising that predict() used on the original data > would predict the fitted results. > > > Dan > > Daniel Nordlund, PhD > Research and Data Analysis Division > Services & Enterprise Support Administration > Washington State Department of Social and Health Services > >> -----Original Message----- >> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Matej >> Zuzcák >> Sent: Tuesday, August 16, 2016 1:42 PM >> To: PIKAL Petr >> Cc: r-help@r-project.org >> Subject: Re: [R] Need help with use of ROCK algorithm in R for binary data >> >> Hi, >> >> thank you very much for your reply. :-) >> >> - So I have really only four objects in this data set. It 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 >> >> - I have modified standard separator for CSV file from comma to | because I >> do other specific parsing and etc. Original data have integer values 1 >> (TRUE) >> and 0 (FALSE). >> >> - Now I use this procedure for convert 1 and 0 on TRUE/FALSE coding (see >> above) without duplicities: >> >> dummyVar <- db[-1] > 0 >> x <- dummyVar >> >> - Result is the same as in my previous mail. Result is the same (in my last >> message) too when I use predict or fitted (rp <- predict(rc, x) / rf <- >> fitted(rc)). Do you know what is different between predict and fitted please? >> And what value of beta and theta parameter is optimal please? So my >> clusters are: ABC - cluster 1, DEF - cluster NA. What is means with "NA"? So >> these objects (ABC, DEF) are the most similar. I will apply this algorithm on >> next set of data, it includes much more objects... I will have question about >> Proximus algorithm yet (in next mail), because it will be second algorithm >> for >> binary clustering of my data sets... >> >> Thanks. >> >> -- >> >> Best Regards >> Matej Zuzcak >> >> Dňa 16.8.2016 o 8:42 PIKAL Petr napísal(a): >> >>> Hi >>> >>> see in line >>> >>>> -----Original Message----- >>>> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Matej >>>> Zuzčák >>>> Sent: Monday, August 15, 2016 11:23 AM >>>> To: r-help@r-project.org >>>> Subject: [R] Need help with use of ROCK algorithm in R for binary >>>> data >>>> >>>> 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| >>> Better to show your data with dput command. Just copy the output of >>> >>> dput(header(db, 20)) >>> >>> to your mail. >>>> 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/ >>>> Ro >>>> ckCluster#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). >>> Hm. Why do you use csv if you set the separator to "|". I would use >> read.table. >>>> 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? >>> Hm. Strange. In help page the result is TRUE/FALSE coding. Again posting >> real data would help us to understand your problem. >>> x <- as.integer(sample(3,10,rep=TRUE)) >>>> x >>> [1] 1 1 1 3 1 3 1 3 2 2 >>>> as.dummy(x) >>> [,1] [,2] [,3] >>> [1,] TRUE FALSE FALSE >>> [2,] TRUE FALSE FALSE >>> [3,] TRUE FALSE FALSE >>> [4,] FALSE FALSE TRUE >>> [5,] TRUE FALSE FALSE >>> [6,] FALSE FALSE TRUE >>> [7,] TRUE FALSE FALSE >>> [8,] FALSE FALSE TRUE >>> [9,] FALSE TRUE FALSE >>> [10,] FALSE TRUE FALSE >>> attr(,"levels") >>> [1] "1" "2" "3" >>> >>> As I understand from help page, each columns is repeated the >> levels(column) times and each column in result has coding T/F based on that >> particular factor level. >>>> 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? >>> There are only 2 clusters with levels 1 and NA. ABC belongs to cluster 1, >>> DEF >> belongs to cluster NA. An what is the most weird, you have only 6 values in >> your db data ??? >>> So again presenting your data either by dput or str is vital for evaluating >> your problem. >>> And BTW do not post in HTML, your messages are more or less scrambled. >>> >>> Cheers >>> Petr >>> >>> >>>> 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. >>> ________________________________ >>> Tento e-mail a jakékoliv k němu připojené dokumenty jsou důvěrné a jsou >> určeny pouze jeho adresátům. >>> Jestliže jste obdržel(a) tento e-mail omylem, informujte laskavě >> neprodleně jeho odesílatele. 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