Positioning of Czech accountants towards IFRS implementation

The paper aims to ascertain the level of harmonization of accounting in small and medium enterprises in the Czech Republic and the impacts arising from the harmonization process. On the basis of the conducted survey, companies show a reserved interest in reporting according to IFRS. As a decisive reason for adopting IFRSs as a reporting framework for a company the adoption shall brought out some gains. Only minority of companies considers the greatest advantage for using IFRS in the comparability of financial statements, strengthening the credibility of financial statements and strengthening the prestige of the business

Classical and recent approaches in cluster analysis

The paper focuses on the development of selected approaches in cluster analysis. There are recently proposed similarity measures for objects characterized by nominal variables, development of algorithms for k-clustering and development of methods for clustering large data files and categorical data. As concerns algorithms for k-clustering, attention is paid to take into account the uncertainty in classifying objects into clusters, namely FCM (fuzzy k-means), PCM, FPCM, RCM, RFCM and RFPCM algorithms. For large data files, algorithms CURE, ROCK, CLARA, CLARANS and BIRCH are included, for categorical data clustering there are COOLCAT and ROCK algorithms. Two-step cluster analysis to cluster large data sets with variables of different types is mentioned

Cluster Analysis of Economic Datas

In the paper, some classical and recent approaches to cluster analysis are discussed. Over the last decades researchers focused mainly on categorical data clustering, uncertainty in cluster analysis and clustering large data sets. In this paper some of the recently proposed techniques are introduced, such as similarity measures for data files with nominal variables, algorithms which include uncertainty in clustering, and the method for data files with many objects

2015 IFCS Conference

International Federation of Classification Societies 2015 (IFCS

Evaluation of Economic Education from Graduates’ Point of View

The labour market expects graduates with certain levels of competences, which reflect the quality of education. In this paper we present the results of the analyses of selected indicators concerning of the education quality obtained on the basis of answers of graduates of the University of Economics, Prague. The graduates were addressed four or five years after graduation within large REFLEX surveys realized in 2006 and 2010. We compare competence levels acquired by graduates with competence levels required by employers; both types of levels were evaluated by graduates. We investigate dependency, agreement and similarity of acquired and requiredcompetence levels by different coefficients and we compare their values in the 2006 and 2010 surveys

Cluster analysis of households characterized by categorical indicators

In the paper we deal with evaluation of the results of cluster analysis which is applied to data files in which objects are characterized qualitative variables. We describe methods of clustering, determination of optimal cluster numbers, and evaluation of obtained clusters implemented in the procedure for two-step cluster analysis in the SPSS statistical software package. These techniques are applied to the selected household indicators gathered in the SILC (Statistics on Income and Living Conditions) survey in the Czech Republic in 2008. We clustered households characterized by the indicators expressing if a household owns a computer and a car as an example. We discuss the problem of determination of optimal cluster numbers by the approach based on information criteria (we use the Bayesian information criterion) and determine number of clusters by means of the silhouette coefficient. Then we describe four obtained clusters on the basis of indicators of working activity, degree of education and degree of urbanization. Moreover, we extended characterizing variables to the recoded indicators expressing how the household goes well with its income. On the basis of this example we illustrate investigation of variable importance. In this case we describe obtained three clusters by three variables used in the analysis. In conclusion we mention some other approaches to evaluation of clustering objects characterized by categorical variables. They consist in both coefficients based on multivariate analysis of variance with using specialized variability measure for nominal and ordinal data, and modification of some other coefficients for qualitative data. The problem of mixed type variables is also mentioned