NDLI: Identification of Core, Semi-Core and Redundant Attributes of a Dataset

Content Provider	IEEE Xplore Digital Library
Author	Hashemi, R.R. Bahrami, A. Smith, M. Young, S.
Copyright Year	2011
Abstract	Data reduction is an essential step in pre-processing of a dataset and it is necessary for improving data quality and obtaining the relevant data from the dataset. Data reduction is performed by identifying and removing redundant attributes of the dataset. However, every non-redundant attribute does not have the same level of contribution to the decision (dependent variable). Therefore, the non-redundant attributes may be further divided into two sub-categories of core (attributes that totally contribute to the decision) and semi-core (attributes that partially contribute to the decision) attributes. In this paper, a methodology for separating core, semi-core, and redundant attributes is introduced and tested. The result shows that the proposed methodology has a high potential for use in any generalization process.
Starting Page	580
Ending Page	584
File Size	829434
Page Count	5
File Format	PDF
ISBN	9781612844275
DOI	10.1109/ITNG.2011.106
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic cores Information gain Noise Artificial neural networks Cluster Quality VSOM clustering Biochemistry Entropy Stress SOM Heating Rough sets Data Reduction Core attribute Redundant Attribute Semi-core attribute
Content Type	Text
Resource Type	Article

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