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A COMPARATIVE STUDY ON THE EFFECTIVENESS OF MULTIDIMENSIONAL ANALYSIS MODELS FOR THE FORECASTING OF FIRM BANKRUPCY (Porównanie skutecznosci modeli wielowymiarowej analizy dyskryminacyjnej sluzacych prognozowaniu upadlosci przedsiebiorstw)

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Mirowska D., Lasek M.
Optimum. Studia Ekonomiczne
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2010
|
issue 1(45)
73-90
EN
In the article, comparative study of multidimensional discriminant analysis models elaborated for firms bankruptcy forecasting under Polish economic conditions are discussed. Bankruptcy forecasting accuracy of the models was verified utilizing financial data from financial statements drawn up for companies operating in the construction branch (construction industry) and calculated firm financial ratios. In the models evaluation, firm classification matrix and the model valuation measures (efficiency and classification errors) were applied. Findings of other studies, described in the literature, on the effectiveness of multidimensional discriminant analysis models for firm bankruptcy forecasting, that were described in the literature including the author's own findings have been discussed briefly. Directions for possible further studies that could lead to an improvement of multidimensional discriminant analysis models for firm bankruptcy forecasting have been proposed.
2

DEVELOPMENT OF DATA MINING MODELS FOR ANALYSIS OF CHARACTERISTICS OF UNEMPLOYED PERSONS AND PREDICTING OF UNEMPLOYMENT DURATION (Polish title below)

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Lasek M., Peczkowski M.
Optimum. Studia Ekonomiczne
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2012
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issue 1(55)
119-142
EN
(Polish title: Budowa modeli eksploracji danych (data mining) na potrzeby analiz cech charakteryzujacych osoby bezrobotne i przewidywania pozostania osoba bezrobotna). The article discusses data mining models that can be useful in the analysis of attributes influencing individual unemployment and can help to estimate the probability with which persons with certain characteristics (age, education, gender, family background, family status, family size etc.) are more likely to be unemployed or to lose their job. Three models were constructed, and verified, to accomplish this task: logistic regression, multilayered neural network and decision tree. The study was based on Polish official statistical data. SAS Enterprise Miner software from SAS Institute Inc. was applied for model construction and data analysis.
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THE COMPLEX APPROACH TO THE IMPROVEMENT OF COMPANY'S ACTIONS THROUGH PROJECTING AND OPTIMIZATION OF ECONOMIC PROCESSES. THE EXAMPLE OF USE OF THE IGRAFX ENTERPRISE MODELER/CENTRAL PROGRAMME (Polish title - below)

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Lasek M., Otmianowski B.
Ekonomia. Rynek, Gospodarka, Społeczeństwo
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2009
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issue 22
3-17
EN
( Title in Polish - 'Kompleksowe podejscie do doskonalenia dzialalnosci firmy poprzez projektowanie i optymalizacje procesów gospodarczych. Przyklad zastosowania programu iGrafx Enterprise Modeler/Central'). This article introduces the way how to design the company's construction, so that it creates the structure built up from fully co-operating business processes. The complex improvement of the business was described through designing and optimization of the actions. The business processes are oriented on the strategy of monitoring and realization of the operating functions. The approach of creating the company, based on designing and optimization of the business actions, was described using the example of the use of the iGrafx Enterprise Modeler/Central programme. This programme is recognised by Gartner as a leader of the computer programmes managed by the BPM (Business Process Management) processes.
4

Zastosowanie metod analizy skupien w przeprowadzaniu segmentacji klientow na potrzeby kampanii reklamowych

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Kutera M., Lasek M.
Contemporary Economics
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2010
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issue 3(15)
163-185
EN
Clustering methods are recently so advanced elaborated algorithms for large collection data analysis that they have been already included today to data mining methods. Clustering methods are nowadays larger and larger group of methods, very quickly evolving and having more and more various applications. In the article, our research concerning usefulness of clustering methods in customer segmentation to manage advertisement campaign is presented. We introduce results obtained by using four selected methods which have been chosen because their peculiarities suggested their applicability to our purposes. One of the analyzed method - k-means clustering with random selected initial cluster seeds gave very good results in customer segmentation to manage advertisement campaign and these results were presented in details in the article. In contrast one of the methods (hierarchical average linkage) was found useless in customer segmentation. Further investigations concerning benefits of clustering methods in customer segmentation to manage advertisement campaign is worth continuing, particularly that finding solutions in this field can give measurable profits for marketing activity.
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