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  • Statistics In Transition

 

Article | 06-July-2017

NEW METHOD OF VARIABLE SELECTION FOR BINARY DATA CLUSTER ANALYSIS

Cluster analysis of binary data is a relatively poorly developed task in comparison with cluster analysis for data measured on stronger scales. For example, at the stage of variable selection one can use many methods arranged for arbitrary measurement scales but the results are usually of poor quality. In practice, the only methods dedicated for variable selection for binary data are the ones proposed by Brusco (2004), Dash et al. (2000) and Talavera (2000). In this paper the efficiency of

Jerzy Korzeniewski

Statistics in Transition New Series, Volume 17 , ISSUE 2, 295–304

Article | 22-July-2019

THE EFFECT OF BINARY DATA TRANSFORMATION IN CATEGORICAL DATA CLUSTERING

This paper focuses on hierarchical clustering of categorical data and compares two approaches which can be used for this task. The first one, an extremely common approach, is to perform a binary transformation of the categorical variables into sets of dummy variables and then use the similarity measures suited for binary data. These similarity measures are well examined, and they occur in both commercial and non-commercial software. However, a binary transformation can possibly cause a loss of

Jana Cibulková, Zdenek Šulc, Sergej Sirota, Hana Rezanková

Statistics in Transition New Series, Volume 20 , ISSUE 2, 33–47

Article | 06-July-2017

PREDICTION OF A FUNCTION OF MISCLASSIFIED BINARY DATA

Noriah M. Al-Kandari, Partha Lahiri

Statistics in Transition New Series, Volume 17 , ISSUE 3, 429–447

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