A generic neural network approach for filling missing data in data mining

Document Type

Conference Proceeding

Publication Date

11-24-2003

Abstract

The recent advances in data mining have produced algorithms for extracting hidden and potentially useful knowledge in large data sets, which are assumed to be complete and reliable. However, data suitable for mining comes from various sources, has different formats, and can have missing or incorrect values [2]. Incomplete data sets significantly distort mining results. Therefore, data preparation to taking care of missing or out-of-range values is very critical to knowledge discovery [8]. This paper proposes a generic framework for missing data imputation using neural networks, where two-stage filling algorithms are implemented. An empirical evaluation of this method through a large credit card data set is performed.

Identifier

0242492174 (Scopus)

Publication Title

Proceedings of the IEEE International Conference on Systems Man and Cybernetics

ISSN

08843627

First Page

862

Last Page

867

Volume

1

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