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Permalink Reply by Edith Ohri on March 30, 2009 at 8:52am

Any link to SOM sources?

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Permalink Reply by Ralph Winters on May 20, 2009 at 7:26pm

Would not be my tool of choice, however here is a link to a SOM in Excel

http://www.geocities.com/adotsaha/NN/SOMinExcel.html

-Ralph Winters

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Permalink Reply by Jean-Francois Allaire on March 30, 2009 at 8:42am

Multiple Correspondence analysis may be used as a preliminary step of clustering analysis. SAS PROC CORRESP followed by PROC FASTCLUS OR PROC CLUSTER is an interesting combination.

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Permalink Reply by ANINDO CHAKRABORTY on May 4, 2009 at 12:17am

Firstly, Thanks to ALL of you for all the valuable suggestions. I have been working on this on and off for last couple of months, hence the delay.

I tried out something very simple since our clients wanted to see "something" very quick. I created dummy (1 or 0) variables from the categorical variables. For e.g. xi=1 if brand=i is purchased and xi=0 otherwise. With this I ended up with ~30 variables. I also had some numeric vars (like distance to closest competitor, guest scores etc) which I left aside for the time being since the clients were more interested in the dummy variables than the others. I derived 3 principal components from these dummy variable space. Once I was satisfied with these princomps, I used them to cluster guests ending with 6 clusters. As a sanity check, I ran an anova on these 6 groups for each of the numeric variables to ensure there was a significant difference in this numeric variable across all 6 groups. All the anova results showed that at least one cluster was different from the rest. The results were received well but I know, we can do lot better to improve the results. Do let me know your thoughts.

But I'd definitely like to try some of the suggestions you've made e.g. creating the dissimilarity matrix, using the cohesion measure. I am studying these techniques, so any help would be welcome!

Lastly, one of my team mates has access to SAS EM and he let me know that SOM was also giving great results. It made the clustering output more visually appealing. But it remains to be seen how does it compare with other techniques. I guess running tests would be the only way to know :-)

Thanks again.

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Permalink Reply by Bruno M on May 4, 2009 at 9:27am

Anindo,

Here is a partial list of free open source predictive analytics tools that are out there that can help you with clustering categorical values using Decision Trees or other methods

http://www.brunocm.com/blog/data-exploration-tools/

Hope this helps.

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Permalink Reply by Hariharan Sunder on August 5, 2010 at 12:49am

Hi Anindo,

I working on similar kind of project, I would like to know how you performed Factor Analysis on Binary data. I have Base SAS and tried Proc factor,Proc Princomp but for binary data they dont seem to work.
Finally I am now trying Correspondance Analysis (Proc Corresp) but im not able to interpret the output.
Any help is appreciated.

Thanks,
Hari

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Permalink Reply by ratheen chaturvedi on February 26, 2013 at 9:01pm

Anindo can you please share what you did post the princomp part and how did you calculate the cluster distance etc. Code snippets or web examples will be deeply appreciated

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Permalink Reply by ss on May 4, 2009 at 10:59pm

hi sir,
im dng my phd in clustering area.i need some research tiltes in clustering.just nw i have registered.so p[lz provide me the details to my mail id s1k1_sk@yahoo.com
thanking u

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Permalink Reply by Ramon Wartala on May 13, 2009 at 3:34am

please have a look to www.co2alarm.com. It is a clustering application on text mining results. The web site is green-centric but the algorithm is domain independend. It is a small ruby on rails application. What kind of data do you have?

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Permalink Reply by Jummai Yusuf on April 28, 2010 at 6:10am

i wnt to be like you. a cluster numeric expertry

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Permalink Reply by Kevin Gray on August 23, 2010 at 6:58pm

In the past I've used matching coefficients, multiple correspondence analysis followed by k-means and "canonical cluster analysis", which uses optimal scaling as the first step. Nowadays I leans towards latent class.

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Permalink Reply by vinay kulkarni on August 26, 2010 at 6:13am

hi,

you can use proc distance algorithm in SAS 9.2 version only..

i assume all your vars are symmetric nominal so you can use Matching distance (DMATCH),if it is asymmetric nominal then Jaccard distance.

the code is :

PROC DISTANCE DATA= Library.Dataset OUT=Library.Dataset_out method=Dmatch;
VAR
var1_im
var2_im
var3_im
var4_im....varn_im;
ID id;
RUN;

here the id(respondent id) should be character.

then clustering code:

Proc cluster data=Library.Dataset_out method=ward pseudo outtree=Library.Dataset_out_tree;
var
_id1
_id2
_id3
_id4
..._idn;
id id;
run;


let me know if you have any doubts

vinay

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