Rough Classification in Incomplete Databases by Correlation Clustering
- https://doi.org/10.2991/ifsa-eusflat-15.2015.95How to use a DOI?
- Incomplete databases, rough classification, correlation clustering, harmony search.
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/ifsa-eusflat-15.2015.95 ID - Aszalos2015/06 ER -