Pattern-Based Target Selection Applied to Fund Raising

Wim Pijls, Rob Potharst and Uzay Kaymak ()

ERIM Report Series Research in Management from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam

Abstract: This paper proposes a new algorithm for target selection. This algorithm collects all frequent patterns (equivalent to frequent item sets) in a training set. These patterns are stored e?ciently using a compact data structure called a trie. For each pattern the relative frequency of the target class is determined. Target selection is achieved by matching the candidate records with the patterns in the trie. A score for each record results from this matching process, based upon the frequency values in the trie. The records with the best score values are selected. We have applied the new algorithm to a large data set containing the results of a number of mailing campaigns by a Dutch charity organization. Our algorithm turns out to be competitive with logistic regression and superior to CHAID.

Keywords: datamining; frequent item sets; frequent patterns; target selection (search for similar items in EconPapers)
JEL-codes: C89 M M11 R4 (search for similar items in EconPapers)
Date: 2001-10-18
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Persistent link: https://EconPapers.repec.org/RePEc:ems:eureri:117

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