Rough Classification in Incomplete Databases by Correlation Clustering

DOI

10.2991/ifsa-eusflat-15.2015.95How to use a DOI?

Keywords

Incomplete databases, rough classification, correlation clustering, harmony search.

Abstract

In the context of data mining, missing data can be handled in several ways. The most common is the artificial construction of missing data, but we can predict it, or transform the whole database in a fuzzy way. In this article we propose a different approach: we extend our rough classification to incomplete databases. This uses the correlation clustering as a tool, which uses a tolerance relation of the similarity of the objects in the database. This relation can be generated from the distance between objects and can be sensitized based on missing data. We demonstrate our method with the wine database.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Laszlo Aszalos
AU  - Tamás Mihálydeák
PY  - 2015/06
DA  - 2015/06
TI  - Rough Classification in Incomplete Databases by Correlation Clustering
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 667
EP  - 674
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.95
DO  - 10.2991/ifsa-eusflat-15.2015.95
ID  - Aszalos2015/06
ER  -