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Ensemble Approach for Clustering of Interval-Valued Symbolic Data

100%

Pelka M.

Statistics in Transition new series

2012

vol. 13

issue 2

335-342

Ensemble approach has been applied with a success to regression and discrimination tasks [see for example Gatnar 2008]. Nevertheless, the idea of ensemble approach, that is combining (aggregating) the results of many base models, can be applied to cluster analysis of symbolic data. The aim of the article is to present suitable ensemble clustering based on symbolic data. The empirical part of the paper presents results simulation studies (based on artificial data sets with known cluster structure) of ensemble clustering based on co-occurrence matrix for symbolic interval-valued data, compared with single clustering method. The results are compared according to corrected Rand index.

Regression analysis for interval-valued symbolic data versus noisy variables and outliers

80%

Pełka M., Dudek A.

Econometrics. Ekonometria. Advances in Applied Data Analytics

2016

issue 2 (52)

35-42

Regression analysis is perhaps the best known and most widely used method used for the analysis of dependence; that is, for examining the relationship between a set of independent variables (X’s) and a single dependent variable (Y). In general regression, the model is a linear combination of independent variables that corresponds as closely as possible to the dependent variable [Lattin, Carroll, Green 2003, p. 38]. The aim of the article is to present two suitable adaptations for a regression analysis of symbolic interval-valued data (centre method and centre and range method) and to compare their usefulness when dealing with noisy variables and/or outliers. The empirical part of the paper presents the results of simulation studies based on artificial and real data, without noisy variables and/or outliers and with noisy variable and outliers. The results are compared according to the values of two coefficients of determination 2 RL and 2 . RU The results show that usually the centre and range method obtains better results even when the data set contains noisy variables and outliers, but in some cases the centre method obtains better results than the centre and range method.

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1 Econometrics. Ekonometria. Advances in Applied Data Analytics

1 Statistics in Transition new series

1 2016

1 2012

1 Dudek A.

1 Pelka M.

1 Pełka M.

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Ensemble Approach for Clustering of Interval-Valued Symbolic Data

Regression analysis for interval-valued symbolic data versus noisy variables and outliers