Authors:
J. Calero
1
;
G. Delgado
1
;
M. Sánchez-Marañón
1
;
D. Sánchez
1
;
M. A. Vila
1
and
J. M. Serrano
2
Affiliations:
1
University of Granada, United States
;
2
University of Jaen, Spain
Keyword(s):
Expert soil knowledge, aggregated soil databases, imprecision factors in soil knowledge, fuzzy data mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
In this work, we start from a database built with soil information from heterogeneous scientific sources (Local Soil Databases, LSDB). We call this an Aggregated Soil Database (ASDB). We are interested in determining if knowledge obtained by means of fuzzy association rules or fuzzy approximate dependencies can represent adequately expert knowledge for a soil scientific, familiarized with the study zone. A master relation between two soil attributes was selected and studied by the expert, in both ASDB and LSDB. Obtained results reveal that knowledge extracted by means of fuzzy data mining tools is significatively better than crisp one. Moreover, it is highly satisfactory from the soil scientific expert’s point of view, since it manages with more flexibility imprecision factors (IFASDB) commonly related to this type of information.