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Keep It Simple: A Case-Base Maintenance Policy Based on Clustering and Information Theory
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Keep It Simple: A Case-Base Maintenance Policy Based on Clustering and Information Theory
Qiang Yang2, 3 and Jing Wu2, 3
| (2) |
School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, V5A 1S6 |
| (3) |
Department of Computer Science, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1 |
Abstract
Today’s case based reasoning applications face several challenges. In a typical application, the case bases grow at a very
fast rate and their contents become increasingly diverse, making it necessary to partition a large case base into several
smaller ones. Their users are overloaded with vast amounts of information during the retrieval process. These problems call
for the development of effective case-base maintenance methods. As a result, many researchers have been driven to design sophisticated
case-base structures or maintenance methods. In contrast, we hold a different point of view: we maintain that the structure
of a case base should be kept as simple as possible, and that the maintenance method should be as transparent as possible.
In this paper we propose a case-base maintenance method that avoids building sophisticated structures around a case base or
perform complex operations on a case base. Our method partitions cases into clusters where the cases in the same cluster are
more similar than cases in other clusters. In addition to the content of textual cases, the clustering method we propose can
also be based on values of attributes that may be attached to the cases. Clusters can be converted to new case bases, which
are smaller in size and when stored distributedly, can entail simpler maintenance operations. The contents of the new case
bases are more focused and easier to retrieve and update. To support retrieval in this distributed case-base network, we present
a method that is based on a decision forest built with the attributes that are obtained through an innovative modification
of the ID3 algorithm.
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