Hello (note that this is the same Peter Flom at a different address with a new e-mail and a new job)
I have a data set with about 800 people and about 1000 variables. The variables are all 'features' of EEG data that have been extracted by subject matter experts in neurology as being potentially useful. All variables have been standardized to mean 0, sd 1. There are many high correlations among them. We are interested in many aspects of this data - one primary aim is to use the EEG data to better classify people who have neurological problems. Two methods that seem particularly relevant to this list are clustering and decision trees. I've done a bit of both, but always on data sets with FAR fewer variables (e.g. about 10 variables). Especially with regard to clustering, I was thinking of doing a principal components analysis prior to the cluster analysis (perhaps with SAS PRINCOMP, FACTOR, or VARCLUS). With regard to trees, I've done some 'basic' analysis of other data sets using R's 'party' and 'rpart' packages. With those data sets, however, the main goal was explanation, and so, I did not explore bagging and boosting and such. Any pointers or introductions to that literature would be most welcome (preferably at a not TOO high mathematical level - I had some calculus many years ago, but am much more interested in applications than in 'theorem-proof' material). I will be exploring this data set for quite some time, so am willing to invest some effort to learn best practices, and am also willing to try a variety of methods. Finally, as to why I am looking at both trees and clusters - partly, we know the diagnosis of the people (hence trees are useful) but we also know that there are difficulties with the diagnoses, and that these difficulties may be amenable to exploration with sophisticated methods Thanks in advance Peter Flom Brainscope, Inc. ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l
