Technological advancement across human activities has brought about accelerated generation of huge amounts of data. Consequently, researchers are faced with the problem how to determine adequate ways of turning the available data mass into useful knowledge. Data analysis adapted to these changes when data mining was developed as an approach to data analysis from different perspectives which reveals significant hidden regularities. This paper presents conceptual characteristics of decision tree, an important data mining method which is, due to its explorative nature, exceptionally suitable for detection of data structure when analysing various problem situations. The empirical section of the paper demonstrates applicative characteristics of this method using CHAID algorithm in leadership studies: an interdependence of selected personal characteristics and the manager’s leadership style has been investigated. The aim of the paper is to develop a classification model for identification of the dominant leadership style. The study was conducted on the sample of 417 managers of privately owned small-sized enterprises in Serbia, using a specially designed questionnaire. The classification model identified the set of six statistically significant personal characteristics as predictors of dominant leadership style.
Ivelina Stefanova Balabanova, Georgi Ivanov Georgiev, Stanimir Michaylov Sadinov and Stela Savova Kostadinova
Imitation modelling processes of telegraphic systems on the Markov chains with unlimited and limited queues were made. For this purpose, the Java modeling tool simulation environment is used. With a fixed number of client stations and a number of system users, data are accumulated about the telegraphic system parameters as: customer ID, arrival time, server ID and exit system. Artificial neural networks (ANN) with backpropagation algorithm and decision tree (DT) method for identification of the studied Markov chains in MATLAB were applied. Training of the structural identification models to determine of the membership of the obtained parameters in telegraphic simulation to both unlimited and limited systems was carried out. The results of the training and synthesis of ANN and DT models are presented. Sufficient results have been obtained for telegraphic identification confirming the successful application of the proposed synthesized classification models, approximately 91% for DT and 99.2% for ANN.
Monica Mihaela Maer Matei, Cristina Mocanu and Ana-Maria Zamfir
Education is a key factor that can contribute to the economic growth, supporting the social mobility and the living standard improvement. Both from the scientific point of view, as well as from the policy making process point of view, it is essential to know how individuals choose their educational path, in order to understand what is and can be the role of different educational routes in ensuring social mobility and improving standard of living. In this article we explore the factors that explain attitudes and decisions of individuals for vocational vs. general education in Romania. Our analysis is based on data from a national survey among adult Romanian population. Attitudes regarding the choice of vocational vs. general education are analysed by employing decision trees method in order to assess the extent to which vocational education is considered a valuable education path or an educational alternative for those with lower socio-economic background.
Silvana Tomic Rotim, Jasminka Dobsa and Zdravko Krakar
This paper offers a brief overview of the research of ICT utilization and benefits of its usage. The results of several important studies conducted worldwide are presented. One of them is a study by the World Economic Forum that we use as the basis of our research. This study covers 134 countries, NRI (Network Readiness Index) is used as a parameter to distinguish the readiness of different countries to adopt ICT. NRI consists of 68 indicators that are organized into three groups. Each group describes one of the three main factors crucial for effective utilization of ICT: Environment, Readiness and Usage. The observed countries are divided into four groups (leaders, followers, league III and league IV) and classification by a decision tree is conducted. The decision tree method is applied to each of the three main factors and the results are presented by means of F1 measure.
Ivan Horvat, Mirjana Pejić Bach and Marjana Merkač Skok
Background: Fraud attempts create large losses for financing subjects in modern economies. At the same time, leasing agreements have become more and more popular as a means of financing objects such as machinery and vehicles, but are more vulnerable to fraud attempts. Objectives: The goal of the paper is to estimate the usability of the data mining approach in discovering fraud in leasing agreements. Methods/Approach: Real-world data from one Croatian leasing firm was used for creating tow models for fraud detection in leasing. The decision tree method was used for creating a classification model, and the CHAID algorithm was deployed. Results: The decision tree model has indicated that the object of the leasing agreement had the strongest impact on the probability of fraud. Conclusions: In order to enhance the probability of the developed model, it would be necessary to develop software that would enable automated, quick and transparent retrieval of data from the system, processing according to the rules and displaying the results in multiple categories.
According to several studies, the European population is rapidly aging far over last years. It is therefore important to ensure that aging population is able to live independently without the support of working-age population. In accordance with the studies, fall is the most dangerous and frequent accident in the everyday life of aging population. In our paper, we present a system to track the human fall by a visual detection, i.e. using no wearable equipment. For this purpose, we used a Kinect sensor, which provides the human body position in the Cartesian coordinates. It is possible to directly capture a human body because the Kinect sensor has a depth and also an infrared camera. The first step in our research was to detect postures and classify the fall accident. We experimented and compared the selected machine learning methods including Naive Bayes, decision trees and SVM method to compare the performance in recognizing the human postures (standing, sitting and lying). The highest classification accuracy of over 93.3% was achieved by the decision tree method.
Emmanuel Asuming Frimpong, Philip Yaw Okyere and Johnson Asumadu
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Maria Tsami, Giannis Adamos, Eftihia Nathanail, Evelina Budilovich Budiloviča, Irina Yatskiv Jackiva and Vissarion Magginas
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Anna Justyna Milewska, Dorota Jankowska, Urszula Cwalina, Dorota Citko, Teresa Więsak, Brian Acacio and Robert Milewski
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