NIP - An Imperfection Processor to Data Mining datasets

DOI

10.1080/18756891.2013.818184How to use a DOI?

Keywords

Low quality data, imprecise/uncertain data, dataset with low quality data, Soft Computing, software tool for Soft Computing

Abstract

Every day there are more techniques that can work with low quality data. As a result, issues related to data quality have become more crucial and have consumed a majority of the time and budget of data mining projects. One problem for researchers is the lack of low quality data in order to test their techniques with this data type. Also, as far as we know, there is no software tool focused on the create/manage low quality datasets which treats, in the widest possible way, the low quality data and helps us to create repositories with low quality datasets for testing and comparison of data mining techniques and algorithms. For this reason, we present in this paper a software tool which can create/manage low quality datasets. Among other things, the tool can transform a dataset by adding low quality data, removing and replacing any data, constructing a fuzzy partition of the attributes, etc. It also allows different input/output formats of the dataset.

Copyright

© 2017, 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  - JOUR
AU  - JoséM. Cadenas
AU  - M. Carmen Garrido
AU  - Raquel Martínez
PY  - 2013
DA  - 2013/04/29
TI  - NIP - An Imperfection Processor to Data Mining datasets
JO  - International Journal of Computational Intelligence Systems
SP  - 3
EP  - 17
VL  - 6
IS  - Supplement 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.818184
DO  - 10.1080/18756891.2013.818184
ID  - Cadenas2013
ER  -