IMI-BAS BAS
 

BulDML at Institute of Mathematics and Informatics >
ITHEA >
International Book Series Information Science and Computing >
2009 >
Book 08 Classification Forecasting Data Mining >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1201

Title: Methods of Regularities Searching Based on Optimal Partitioning
Authors: Senko, Oleg
Kuznetsova, Anna
Keywords: Optimal Partitioning
Statistical Validity
Permutation Test
Regularities
Explanatory Variables Effect
Complexity
Probability and Statistics
Data Mining
Issue Date: 2009
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: The purpose of discussed optimal valid partitioning (OVP) methods is uncovering of ordinal or continuous explanatory variables effect on outcome variables of different types. The OVP approach is based on searching partitions of explanatory variables space that in the best way separate observations with different levels of outcomes. Partitions of single variables ranges or two-dimensional admissible areas for pairs of variables are searched inside corresponding families. Statistical validity associated with revealed regularities is estimated with the help of permutation test repeating search of optimal partition for each permuted dataset. Method for output regularities selection is discussed that is based on validity evaluating with the help of two types of permutation tests.
URI: http://hdl.handle.net/10525/1201
ISSN: 1313-0455
Appears in Collections:Book 08 Classification Forecasting Data Mining

Files in This Item:

File Description SizeFormat
ibs-08-p20.pdf89.37 kBAdobe PDFView/Open

 



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0!   Creative Commons License DSpace Software Copyright © 2002-2009  The DSpace Foundation - Feedback