Hi, I want to learn more on clustering techniques. I have skimmed through Programming Collective Intelligence and Mahout in Action in the past but I don't have them on hand at the moment... :( I've seen Isabel Drost mail about test data on http://mldata.org/about/ I've had an idea of using http://mldata.org/repository/view/stockvalues/ for a pet project. My idea is as follow: can we see a common behaviour between companies' stock value? I would expect ending up with cluster of banking sector shares, utilities share, media etc... and maybe some more unexpected cluster, who knows?
My idea is basically: 1°)Transform the dataset from values to daily variation as percentage drop/raise (data is then normalized) 2°)Apply clustering technique(s) The issue may seem silly but as I understand it, clustering happens in a 2 (or more) dimension space. I know I have 2 dimensions: variation and time, but I can't wrap my head on the problem... I *think* that the K-Means example does exactly what I intend to do my second step, is this correct? However, I can grasp what the 2 dimensional display represent exactly: what are the x and y axis ? Added question: I am fairly new to the M/R paradigm, but let's say I would like to do step 1 (data normalization) in a M/R fashion. Would the following be a good idea: My data is a matrix of k stock values S in n intervals of time. I call the first stock in the file, first and second period: S1,t & S1,t+1 ... Map Step: input: ((S1,t ... S1,t+n),... ,(Sk,t ... Sk,t+n) ) output (( (S1,t;S1,t+1),...,(S1,t+n-1;S1,t+n)), ... ,( (Sk,t;Sk,t+1),...,(Sk,t+n-1;Sk,t+n)) ) Reduce Step: ( (%S1,t+1.....%S1,t+n), ...,(%S1,t+1.....%S1,t+n)) I apologize for my beginner's questions but.... everyone has to start somewhere :-) BR, Florent Empis