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The Use of Data Mining for Assessing Performance of Administrative Services

The Use of Data Mining for Assessing Performance of Administrative Services
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Author(s): Zdravko Pecar (University of Ljubljana, Slovenia) and Ivan Bratko (University of Ljubljana, Slovenia)
Copyright: 2010
Pages: 16
Source title: Data Mining in Public and Private Sectors: Organizational and Government Applications
Source Author(s)/Editor(s): Antti Syvajarvi (University of Lapland, Finland) and Jari Stenvall (Tampere University, Finland)
DOI: 10.4018/978-1-60566-906-9.ch004

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Abstract

The aim of this research was to study the performance of 58 Slovenian administrative districts (state government offices at local level), to identify the factors that affect the performance, and how these effects interact. The main idea was to analyze the available statistical data relevant to the performance of the administrative districts with machine learning tools for data mining, and to extract from available data clear relations between various parameters of administrative districts and their performance. The authors introduced the concept of basic unit of administrative service, which enables the measurement of an administrative district’s performance. The main data mining tool used in this study was the method of regression tree induction. This method can handle numeric and discrete data, and has the benefit of providing clear insight into the relations between the parameters in the system, thereby facilitating the interpretation of the results of data mining. The authors investigated various relations between the parameters in their domain, for example, how the performance of an administrative district depends on the trends in the number of applications, employees’ level of professional qualification, etc. In the chapter, they report on a variety of (occasionally surprising) findings extracted from the data, and discuss how these findings can be used to improve decisions in managing administrative districts.

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