[R] Need help with use of ROCK algorithm in R for binary data
Matej Zuzčák
mzuzcak at secit.sk
Wed Aug 17 13:51:35 CEST 2016
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-bounces at r-project.org] On Behalf Of Matej
>> Zuzcák
>> Sent: Tuesday, August 16, 2016 1:42 PM
>> To: PIKAL Petr
>> Cc: r-help at 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-bounces at r-project.org] On Behalf Of Matej
>>>> Zuzčák
>>>> Sent: Monday, August 15, 2016 11:23 AM
>>>> To: r-help at 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]]
>>>>
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> ______________________________________________
> R-help at 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
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