Rosa Matias, João-Paulo Moura, Paulo Martins, Fátima Rodrigues



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 disagreement, starting it with new hypnoses or allow data reduction by disable an intermediate cluster from the next stage.


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Paper Citation

in Harvard Style

Matias R., Moura J., Martins P. and Rodrigues F. (2008). SEMI-AUTOMATIC PARTITIONING BY VISUAL SNAPSHOPTS . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 78-86. DOI: 10.5220/0001709400780086

in Bibtex Style

author={Rosa Matias and João-Paulo Moura and Paulo Martins and Fátima Rodrigues},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
SN - 978-989-8111-37-1
AU - Matias R.
AU - Moura J.
AU - Martins P.
AU - Rodrigues F.
PY - 2008
SP - 78
EP - 86
DO - 10.5220/0001709400780086