Technical Report Available.

[Jerome H. Friedman]

                   *** Technical Report Available ***


                  Bump Hunting in High-Dimensional Data

                            Jerome H. Friedman
                            Stanford University

                            Nicholas I. Fisher
                           CMIS - CSIRO, Sydney 
   

                                 ABSTRACT

Many data analytic questions can be formulated as (noisy) optimization
problems. They explicitly or implicitly involve finding simultaneous
combinations of values for a set of ("input") variables that imply
unusually large (or small) values of another designated ("output")
variable. Specifically, one seeks a set of subregions of the input
variable space within which the value of the output variable is
considerably larger (or smaller) than its average value over the
entire input domain. In addition it is usually desired that these
regions be describable in an interpretable form involving simple
statements ("rules") concerning the input values. This paper describes
a new procedure directed towards this goal based on the notion of
"patient" rule induction. This patient strategy is contrasted with the
greedy ones used by most rule induction methods, and semi-greedy ones
used by some partitioning tree techniques such as CART. Applications
involving scientific and commercial data bases are presented.
Keywords: noisy function optimization, classification, association,
rule induction, data mining.


Available by ftp from:
"ftp://stat.stanford.edu/pub/friedman/prim.ps.Z"

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