Authors:
Rosa Matias
1
;
João-Paulo Moura
2
;
Paulo Martins
2
and
Fátima Rodrigues
3
Affiliations:
1
Polytechnic Institute of Leiria, Portugal
;
2
University Of Trás-os-Montes e Alto Douro; Knowledge Engineering and Decision Support Research Center, Portugal
;
3
Porto Institute of Engineering; Knowledge Engineering and Decision Support Research Center, Portugal
Keyword(s):
Visual Data Mining, Spatial Clustering, Information Visualization.
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:
It is stated that a closer intervention of experts in knowledge discovery can complement and improve the effectiveness of results. Normally, in data mining, automated methods display final results through visualization methods. A more active intervention of experts on automated methods can bring enhancements to the analysis; No meanwhile that approach raises questions about what is a relevant stopping stage. In this work, efforts are made to couple automatic methods with visualization methods in the context of partitioning algorithms applied to spatial data. A data mining workflow is presented with the following concepts: data mining transaction, data mining save point and data mining snapshot. Moreover to display results, novel visual metaphors are changed allowing a better exploration of clustering. In knowledge discovery, experts validate final results; certainly it would be appropriate to them validate intermediate results, avoiding, for instance, losing time, when in disagreemen
t, starting it with new hypnoses or allow data reduction by disable an intermediate cluster from the next stage.
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