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1
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On Using Data Mining Techniques for Context-Aware Student Grouping in E-Learning Systems

100%
Zakrzewska D.
Information Systems in Management
|
2014
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vol. 3
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issue 1
77-88
EN
Performance of an e-learning system depends on an extent to which it is adjusted to student needs. Priorities of the last ones may differ in accordance with the context of use of an e-learning environment. For personalized e-learning system based on student groups, different distribution of the groups should be taken into account. In the paper, using of data mining techniques for building student groups depending on the context of the system use is considered. As the main technique unsupervised classification is examined. Context parameters depending on courses and student models are tested. Experiment results for real student data are discussed.
2
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Using multi-objective affinity model for mining the rules of revisits within 72 hours for emergency department patients

89%
Chao-Wen C., Yuh-Wen C., Moussa L., Tzung-Hung L.
Multiple Criteria Decision Making
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2015
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vol. 10
5-31
EN
When patients return to the emergency department (ED) within 72 hours after their previous ED discharge, it is generally assumed that their initial evaluation or treatment had been somehow inadequate. Mining data related to unplanned ED revisits is one method to determine whether this problem can be overcome, and to generate useful guidelines in this regard. In this study, we use the receiver operating characteristic (ROC) curve to compare the data mining model by affinity set to other well known approaches. Some scholars have validated the affinity model for its simplicity and power in handling information systems especially when showing binary consequences. In experimental results, SVM showed the best performance, with the affinity model following only slightly behind. This study demonstrated that when patients visit the ED with normotensive status or smooth breath patterns, or when the physician-patient ratio is moderate, the frequency with which patients revisit the ED is significantly higher.
3
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Using Permutation Tests in Multiple Correlation Investigation

88%
Stelmach J., Uniwersytet Ekonomiczny w Katowicach; Wydział Zarządzania; Katedra Statystyki
Acta Universitatis Lodziensis. Folia Oeconomica
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2012
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vol. 269
EN
An indication of correlation between dependent variable and predictors is a crucial point in building statistical regression model. The test of Pearson correlation coefficient – with relatively good power – needs to fulfill the assumption about normal distribution. In other cases only non-parametric tests can be used. This article presents a possibility and advantages of permutation tests with the discussion about proposed test statistics. The power of proposed tests was estimated on the basis of Monte Carlo experiments. The investigations were carried out for real data – a sample of refinery process parameters, where the indication of changes in correlation, even for sample with small size is very important. It creates an opportunity to react to changes and update statistical models quickly and keep acceptable quality of prediction
4
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THE CONCEPT OF BUSINESS INTELLIGENCE IN THE MICROSOFT SQL SERVER ENVIRONMENT

88%
Szmajduch M.
Information Systems in Management
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2016
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vol. 5
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issue 3
413-423
EN
Application of supporting business-related decision making processes through the use of information systems is becoming one of the fundamental requirements of the market competition. In this paper we present a survey of Business Intelligence (BI) models which can be implemented in Microsoft SQL Server environment. The survey is a response to the rapid development of BI solutions as they enter new areas of company’s activities, adopting new technologies. Business Intelligence systems have become an integral part of every major company. The aim of this analysis is to present the Microsoft SQL Server capabilities, functionalities and services dedicated for the BI purposes. The overview is provided with simple comprehensive analysis of selected environment components indicating their relevance to the particular company requirements. The summary of the significance of using Microsoft SQL Server software is the review of selected services.
5
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Przewidywanie zachowań konsumentów za pomocą analizy scoringowej

75%
Trojanowski M.
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu
|
2018
|
issue 525
83-95
PL
Przewidywanie zachowań nabywców, z jednej strony, jest coraz trudniejsze, z drugiej zaś rosną możliwości techniczne gromadzenia i analizowania wielu danych na temat nabywców (Big Data) oraz odkrywania niewidocznych na pierwszy rzut oka zależności w zachowaniach konsumentów (Data Mining). Celem artykułu jest przedstawienie sposobu przeprowadzenia analizy scoringowej, należącej do grupy analiz predykcyjnych, służących określaniu prawdopodobieństwa wystąpienia pewnych zdarzeń, w tym przypadku pozytywnej reakcji adresatów działań marketingowych. W tekście wykorzystano analizę przypadku organizacji społecznej SOS Wioski Dziecięce, której komunikaty marketingowe skierowane były do potencjalnych donatorów, wybranych na podstawie wyników analizy scoringowej. Analiza ta pokazuje, jak cennym uzupełnieniem tradycyjnych badań marketingowych może być analiza scoringowa. Artykuł jest jednocześnie postulatem szerszego zainteresowania się tym kierunkiem prowadzenia badań i analiz nabywców.
6
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APPLICATION OF SELECTED SUPERVISED CLASSIFICATION METHODS TO BANK MARKETING CAMPAIGN

63%
Grzonka D., Suchacka G., Borowik B.
Information Systems in Management
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2016
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vol. 5
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issue 1
36-48
EN
Supervised classification covers a number of data mining methods based on training data. These methods have been successfully applied to solve multi-criteria complex classification problems in many domains, including economical issues. In this paper we discuss features of some supervised classification methods based on decision trees and apply them to the direct marketing campaigns data of a Portuguese banking institution. We discuss and compare the following classification methods: decision trees, bagging, boosting, and random forests. A classification problem in our approach is defined in a scenario where a bank’s clients make decisions about the activation of their deposits. The obtained results are used for evaluating the effectiveness of the classification rules.
7
Content available remote

Analiza asocjacji i reguły asocjacyjne w badaniu wyborów zajęć dydaktycznych dokonywanych przez studentów. Zastosowanie algorytmu Apriori

51%
Lasek M., Pęczkowski M.
Ekonomia. Rynek, Gospodarka, Społeczeństwo
|
2013
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issue 34
67-88
PL
W artykule przedstawiono możliwości i korzyści zastosowania metody analizy asocjacji, zaliczanej do metod eksploracji danych (ang. Data Mining), w zagadnieniach dotyczących wyborów przez studentów nieobowiązkowych zajęć dydaktycznych. Krótko opisano metodę analizy asocjacji i budowy reguł asocjacyjnych, szczególną uwagę poświęcając algorytmowi Apriori, jednemu z najpopularniejszych algorytmów analizy asocjacji i budowy reguł asocjacyjnych, w sposób uporządkowany i logiczny realizującemu potrzebne działania oraz przystępnie, przejrzyście i zrozumiale obrazującemu ideę analizy asocjacji i generowanie reguł asocjacyjnych. Rozważania zilustrowano przykładem wyboru przez studentów zajęć, spośród prowadzonych na Wydziale Nauk Ekonomicznych Uniwersytetu Warszawskiego we współpracy z SAS Institute Polska, w ramach ich cyklu o nazwie „Data Mining Certificate Program”. Wskazano, wraz z krótką wzmianką o jego działaniu, oprogramowanie wspomagające przeprowadzanie analizy asocjacji, tworzenie reguł asocjacyjnych i interpretację uzyskiwanych wyników – program SAS Enterprise Miner firmy SAS Institute Inc. z USA, wykorzystywany przez nas w zaprezentowanym w artykule zagadnieniu wyboru zajęć dydaktycznych przez studentów.
EN
This article discusses the possibilities and advantages of the association analysis method, belonging to Data Mining, in problems relating to choices made by students from elective courses. Such choices are possible when students can freely choose several courses from separate groups of elective courses. The association analysis method and the construction of association rules are briefly described. Particular attention was paid to the Apriori algorithm. It constitutes one of the most popular algorithms for the association analysis and the construction of association rules. The Apriori algorithm in an orderly and logical manner performs necessary actions as well as accessibly, transparently and understandably reflects the concept of the association analysis and the construction of association rules. The considerations are illustrated using an example of choices made by students from elective courses conducted within the educational path called “Data Mining Certificate Program” at the Faculty of Economic Sciences, Univerity of Warsaw, in cooperation with SAS Institute Polska. The discussed issue of elective courses selection was explored using SAS Enterprise Miner software from SAS Institute Inc. U.S. – the article provides a very short presentation of its functionality, possibilities of performing the association analysis and the construction of association rules, as well as interpretation of the analysis results.
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