Decomposition in Data Mining A Medical Case Study.pdf
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Decomposition in Data Mining: A Medical Case Study
Andrew Kusiak
Intelligent Systems Laboratory
4312 Seamans Center
The University of Iowa
Iowa City, Iowa 52242 ñ 1527
andrew-kusiak@
/~ankusiak
ABSTRACT
Decomposition is a tool for managing complexity in data mining and enhancing the quality of knowledge extracted form
large databases. A typology of decomposition approaches applicable to data mining is presented. One of the decomposition
approaches, the structured rule-feature matrix, is used as the backbone of a system for informed decision-making. Such a
system can be implemented as a decision table, a decision map, or a decision atlas. The ideas presented in the paper are
illustrated with examples and a medical case study.
Keywords: Data mining, decision making, decomposition, rule structuring, disease diagnosis, lung cancer.
1. INTRODUCTION
Data mining is concerned with discovery of patterns, associations, rules, and other forms of knowledge in data sets. This
knowledge is automatically extracted from data rather than being formulated by a user as it is done in traditional modeling
approaches, e.g., statistical or optimization modeling. As a new discipline, data mining draws from other areas such as
statistics, machine learning, database retrieval, pattern recognition, and high performance computing.
In many applications, data is automatically generated and therefore
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