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"Unsupervised Clusteranalysis with rapidminer"

Hi

ive an instance pool with a couple of attributes. I want to classify the instances. Which are the "common" unsupervised clusteranalysis methods that i can use with rapidminer? I cant give the actual outcome for the samples.

On a sidenote: can somebody recommend a readily comprehensible literatur on the topic of unsupervised clusteranalysis?

greez

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el_chief

Hello,

FYI, classification and clustering are different things. Classification is predicting a whole number, while clustering is grouping similar observations into a number of groups.

What is your data like and what are you trying to accomplish?

RapidMiner has 9 clustering methods, but the common ones are k-means (good for huge data sets), agglomerative, expectation maximization, and DBSCAN.

The book Multivariate Data Analysis by Joseph Hair is the most understandable.

Good luck

Neil

shaihulud

well its like that:

i do want to read data from csv files. Each line represents an instance with a name and a couple of attributes. The attribute values are mostly strings and they can be arbitrary. I need to find a way to identify some representatives for each "group" of instances i have in the data. I cannot examine alle the instances ecause i have 10000s of them in my files, so i need to narrow it down as best as i can.
Many of them have similar or equal attribute values so that i can put them into a group (cluster) and just choose one of the instances from each group as a representative. (Again: i do not know the values of the instances or the attributes) My first thought there was to use clusteranalysis, but maybe i am wrong?!

I would appreciate any help.

IngoRM

Hi,

no clustering sounds exactly like something which could help you here. Please post in the Problems & Support board of this forum if you have any questions about this can actually be achieved.

Cheers,
Ingo