Data Dimensionality Reduction Framework for Data Mining

Authors

  • M. Danubianu "Stefan cel Mare" University of Suceava
  • S. G. Pentiuc "Stefan cel Mare" University of Suceava

DOI:

https://doi.org/10.5755/j01.eee.19.4.2043

Keywords:

Data mining, data pre-processing, feature selection

Abstract

The database built by TERAPERS project contains a considerable volume of data about the personal or familial anamnesis, and regarding the process of personalized therapy of dyslalia. This data can be the starting point of data mining processes that could provide useful information for the design and adaptation of different therapies to obtain the maximum efficiency. Because data dimensionality affects the performances of data mining tasks, this paper presents two supervised feature selection methods to be used in the frame of an information system. These methods were validated by experiments in the classification of Romanian patients with speech disorders. Obtained results will be used to implement Logo-DM, which is intended to be a data mining system aiming to optimize the personalized therapy of dyslalia.

DOI: http://dx.doi.org/10.5755/j01.eee.19.4.2043

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Published

2013-03-28

How to Cite

Danubianu, M., & Pentiuc, S. G. (2013). Data Dimensionality Reduction Framework for Data Mining. Elektronika Ir Elektrotechnika, 19(4), 87-90. https://doi.org/10.5755/j01.eee.19.4.2043

Issue

Vol. 19 No. 4 (2013)

Section

SYSTEM ENGINEERING, COMPUTER TECHNOLOGY

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