Marcin Pełka

Ratings-/Rankings-Based Versus Choice-Based Conjoint Analysis for Predicting Choices

Nowadays, for market simulation in consumer markets with multi-attributed products, choice-based conjoint analysis (CBC) is most popular. The popularity stems—on one side—from the possibility to use online-panels for affordable data collection and—on the other side—from the possibility to estimate part worths at the respondent level using only few observations. However, a still open question is, whether this money- and time-saving approach provides the same or even better results than ratings-/rankings-based alternatives. An experiment with 787 students from Poland and Germany is used to answer this question: Cola preferences are measured using CBC as well as ratings-/rankings-based alternatives. The results are compared using the Multitrait-Multimethod Matrix for the estimated part worths and first choice hit rates for holdout choice sets. The experiment shows a superiority of CBC, but also important differences between Polish and German cola consumers that outweigh methodological differences.

Symbolic Multidimensional Scaling Versus Noisy Variables and Outliers

The aim of the paper is to present and compare effectiveness of symbolic multidimensional scaling methods when we are dealing data with noisy variables and/or outliers. In the article basic terms of symbolic data analysis and symbolic multidimensional scaling are presented. In empirical part simulation experiment results with application of Interscal and I-Scal (random and rational start point) are compared based on artificial data (containing noisy variables and/or outliers) generated by cluster.Gen procedure from clusterSim package of R software.

Specialization in Smart Growth Sectors vs. Effects of Change of Workforce Numbers in the European Union Regional Space

The purpose of the study is to identify the relations between the level of specialization in smart growth sectors and the effects of change of workforce numbers in NUTS (The Nomenclature of Territorial Units for Statistics) 2 regions of the European Union countries. Multivariate data analysis methods, structural-geographic shift-share method and regional specialization indices were applied in the study. The structure of workforce in economic sectors, separated based on the intensity of research and development activities in NUTS 2 regions in the period 2009–2012, constituted the subject matter of the analysis. The application of shift-share analysis allowed for determining the effects of workforce structure, competitiveness and number changes in smart growth sectors against the reference area, i.e. the European Union regional area. Multivariate data analysis methods facilitated the typology of the analyzed regions against the level of specialization and the type of effects resulting from the change of workforce numbers in smart growth sector, as well as determining the relations between them.

A Comparison Study for Spectral, Ensemble and Spectral-Mean Shift Clustering Approaches for Interval-Valued Symbolic Data

Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. This paper presents a comparison study for clustering efficiency (according to adjusted Rand index) for spectral, ensemble, and spectral-mean shifted clustering methods for symbolic data. Evaluation studies with application of artificial data with known cluster structure (obtained from mlbench and clusterSim packages of R) show the usefulness and stable results of the ensemble clustering compared to spectral and spectral-mean shift method.

Symbolic Cluster Ensemble based on Co-Association Matrix versus Noisy Variables and Outliers

Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. Ensemble approach based on aggregating information provided by different models has been proved to be a very useful tool in the context of the supervised learning. The main goal of this approach is to increase the accuracy and stability of the final classification. Recently the same techniques have been applied for cluster analysis, where by combining a set of different clusterings, a better solution can be received. Ensemble clustering techniques might be not a new problem, but their application to the symbolic data case is a quite new area. The article presents a proposal of application of the co-association based approach in cluster analysis when dealing symbolic data with noisy variables and outliers. In the empirical part simulation experiment results are compared based on artificial data (containing noisy variables and/or outliers). Besides that ensemble clustering results of real data set is shown (segmentation example). In both cases ensemble clustering results are compared with results obtained from a single clustering method.