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ON THE ROLE OF INTERPRETABILITY IN FUZZY DATA MINING.

Authors :
MENCAR, CORRADO
CASTELLANO, GIOVANNA
FANELLI, ANNA M.
Source :
International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems. Oct2007, Vol. 15 Issue 5, p521-537. 17p. 1 Chart.
Publication Year :
2007

Abstract

Data Mining, a central step in the broader overall process of Knowledge Discovery from Databases, concerns with discovering useful properties, called patterns, from data. Understandability is an essential — yet rarely tackled — feature that makes resulting patterns accessible by end users. In this paper we argue that the adoption of Fuzzy Logic for Data Mining can improve understandability of derived patterns. Indeed, Fuzzy Logic is able to represent concepts in a “human-centric” way. Hence, Data Mining methods based on Fuzzy Logic may potentially meet the so-called “Comprehensibility Postulate”, which characterizes the blurry notion of understandability. However, the mere adoption of Fuzzy Logic for Data Mining is not enough to achieve understandability. This paper describes and comments a number of issues that need to be addressed to provide for understandable patterns. A careful consideration of all such issues may end up in a systematic methodology to discover comprehensible knowledge from data. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02184885
Volume :
15
Issue :
5
Database :
Academic Search Index
Journal :
International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems
Publication Type :
Academic Journal
Accession number :
27018497
Full Text :
https://doi.org/10.1142/S0218488507004856