Suneel, Thanks for the information.
I am using 0.7 packaged with CDH . On Tue, Feb 18, 2014 at 2:14 PM, Suneel Marthi <[email protected]> wrote: > > > > > > > On Tuesday, February 18, 2014 3:37 AM, Bikash Gupta > <[email protected]> wrote: > > Ted/Peter, > > Thanks for the response. > > This is exactly what I am trying to achieve. May be I was not able to > put my questions clearly. > > I am clustering on few variables of Customer/User(except their > customer_id/user_id) and storing customer_id/user_id list in a > separate place. > > Question) What is the approach to identify each member in each cluster > by its unique id. > Answer) I have to run a script post-clustering to map > customer_id/user_id for the clustered output to identify the member > uniquely. > >>> If u r working off of Mahout 0.9 u don't have to do that. The Clustered >>> output should display the vectors with the vectorid (user_id in ur case) >>> that belong to a specfic cluster along with the distance of that vector >>> from the cluster center. > > Correct me if I am wrong :) > > > On Tue, Feb 18, 2014 at 10:53 AM, Ted Dunning <[email protected]> wrote: >> Bikash, >> >> Peter is just right. >> >> Yes, you can cluster on these few variables that you have. Probably you >> should translate location to x,y,z coordinates so that you don't have >> strange geometry problems, but location, gender and age are quite >> reasonable characteristics. You will get a fairly weak clustering since >> these characteristics actually tell very little about people, but it is a >> start. >> >> You *don't* want to cluster using user ID for exactly the reasons that >> Peter mentioned. Another way to put it is that the user ID tells you >> absolutely nothing about the person and thus is not useful for the >> clustering. >> >> You *do* have to retain the assignment of users to cluster and that >> assignment is usually stored as a list of user ID's for each cluster. This >> does not at all imply, however, that the user ID was used to *form* the >> cluster. >> >> >> >> >> On Mon, Feb 17, 2014 at 9:01 PM, Peter Jaumann >> <[email protected]>wrote: >> >>> Bikash, >>> As Ted pointed out already...... >>> You can cluster on all variables except your customer_id. That's your >>> identifier. >>> Customers within a cluster are 'similar'; how similar depends on the >>> fidelity of your clustering. >>> The clustering algorithm uses (you'll choose) some kind of distance, or >>> similarity/dissimilarity >>> measure (which one to use depends on the type of data you have). This >>> measure will, >>> eventually, determine how separate/how unique your clusters are. Goal is to >>> have your clusters distinct >>> from each other but have the cluster members, within a cluster, as similar >>> as possible. >>> >>> In the output, each member in each cluster is uniquely identified by it's >>> customer_id, the cluster it belongs to, >>> and a distance measure that shows (usually) how close, or not, the >>> customer_id is from its cluster center. >>> >>> In terms of what you want to do, my assumption is that you'd like to find >>> out a structure, or patterns, >>> within your customer base, based on a set of variables that you have. This >>> is often called a segmentation. >>> >>> Hope this helps! What you want to do is a pretty basic and straight-forward >>> application of clustering. >>> Good luck, >>> -Peter >>> >>> >>> >>> On Mon, Feb 17, 2014 at 9:48 PM, Bikash Gupta <[email protected] >>> >wrote: >>> >>> > Basically I am trying to achieve customer segmentation. >>> > >>> > Now to measure customer similarity within a cluster I need to >>> > understand which two customer are similar. >>> > >>> > Assumption: To understand these customer uniquely I need to provide >>> > their CustomerId >>> > >>> > Is my assumption correct? If yes then, will customerId affect the >>> > clustering output >>> > >>> > If no then how can I identify customer uniquely >>> > >>> > On Tue, Feb 18, 2014 at 2:55 AM, Ted Dunning <[email protected]> >>> > wrote: >>> > > That really depends on what you want to do. >>> > > >>> > > What is it that you want? >>> > > >>> > > >>> > > On Mon, Feb 17, 2014 at 12:25 PM, Bikash Gupta < >>> [email protected] >>> > >wrote: >>> > > >>> > >> Ok...so UserId is not a good field for this combination, but if I want >>> > >> User Clustering, what should be combination(just for >>> > >> understanding)..... >>> > >> >>> > >> On Tue, Feb 18, 2014 at 1:44 AM, Ted Dunning <[email protected]> >>> > >> wrote: >>> > >> > On Mon, Feb 17, 2014 at 9:00 AM, Bikash Gupta < >>> > [email protected] >>> > >> >wrote: >>> > >> > >>> > >> >> Let say I am clustering users, I am providing their profile data to >>> > >> >> discover similarity between two user. >>> > >> >> >>> > >> >> So my input would be [UserId, Location, Age, Gender, Time Created ] >>> > >> >> >>> > >> >> Now if my UserId length is of minimum 10 characters which is >>> > >> >> comparative very large number than other categorical data. >>> > >> >> >>> > >> > >>> > >> > User id is not a good field for clustering. >>> > >> > >>> > >> > Location is fine if you want geo-graphical clsutering. >>> > >> > >>> > >> > Location + age + gender is fine for geo-demo-graphical clustering. >>> > >> > >>> > >> > Adding time created might give a tiny bit of insight. >>> > >> > >>> > >> > But these fields are not going to lead to great insights. >>> > >> >>> > >> >>> > >> >>> > >> -- >>> > >> Thanks & Regards >>> > >> Bikash Kumar Gupta > >>> > >> >>> > >>> > >>> > >>> > -- >>> > Thanks & Regards >>> > Bikash Kumar Gupta >>> > >>> > > > > -- > Thanks & Regards > Bikash Kumar Gupta -- Thanks & Regards Bikash Kumar Gupta
