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Reduction of Categorical and Numerical Attribute Values for Understandability of Data and Rules
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Reduction of Categorical and Numerical Attribute Values for Understandability of Data and Rules
Yuji Muto1  , Mineichi Kudo1 and Yohji Shidara1
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Division of Computer Science, Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan |
Abstract
In this paper, we discuss attribute-value reduction for raising up the understandability of data and rules. In the traditional
“reduction” sense, the goal is to find the smallest number of attributes such that they enable us to discern each tuple or
each decision class. However, once we pay attention also to the number of attribute values, that is, the size/resolution of
each attribute domain, another goal appears.
An interesting question is like, which one is better in the following two situations 1) we can discern individual tuples with
a single attribute described in fine granularity, and 2) we can do this with a few attributes described in rough granularity.
Such a question is related to understandability and Kansei expression of data as well as rules. We propose a criterion and
an algorithm to find near-optimal solutions for the criterion. In addition, we show some illustrative results for some databases
in UCI repository of machine learning databases.
Keywords Attribute Values - Reduction - Grouping - Granularity - Understandability
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