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Wheigting quantitative and qualitative variables in clustening methods

Author
Gibert, Karina; Cortes, U.
Type of activity
Journal article
Journal
Mathware and soft computing
Date of publication
1997-05
Volume
4
Number
3
First page
251
Last page
266
Abstract
Description of individuals in ill-structured domains produces messy data matrices. In this context, automated classification requires the management of those kind of matrices. One of the features involved in clustering is the evaluation of distances between individuals. Then, a special function to calculate distances between individuals partially simultaneously described by qualitative and quantitative variables is required. In this paper properties and details of the metrics used by Klass in th...
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

Participants