Moving Towards a Methodology Employing Knowledge Discovery in Databases to Assist in Decision Making Regarding Academic Placement and Student Admissions for Universities

2017 ◽  
pp. 215-229 ◽  
Author(s):  
María Isabel Uvidia Fassler ◽  
Andrés Santiago Cisneros Barahona ◽  
Diego Fernando Ávila-Pesántez ◽  
Ivonne Elizabeth Rodríguez Flores
Keyword(s):  
Decision Making ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Academic Placement

Expanding Data Mining Power with System Dynamics

2008 ◽  
pp. 2688-2696
Author(s):  
Edilberto Casado
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Pattern Recognition ◽  
System Dynamics ◽  
Knowledge Discovery ◽  
Strategic Decision ◽  
Knowledge Discovery In Databases ◽  
Strategic Decision Making ◽  
The World ◽  
Mathematical Techniques

Business intelligence (BI) is a key topic in business today, since it is focused on strategic decision making and on the search of value from business activities through empowering a “forward-thinking” view of the world. From this perspective, one of the most valuable concepts within BI is the “knowledge discovery in databases” or “data mining,” defined as “the process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (SPSS, 1997).

Expanding Data Mining Power with System Dynamics

2005 ◽  
pp. 1155-1161
Author(s):  
Edilberto Casado
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Pattern Recognition ◽  
System Dynamics ◽  
Knowledge Discovery ◽  
Strategic Decision ◽  
Knowledge Discovery In Databases ◽  
Strategic Decision Making ◽  
The World ◽  
Mathematical Techniques

Business intelligence (BI) is a key topic in business today, since it is focused on strategic decision making and on the search of value from business activities through empowering a “forward-thinking” view of the world. From this perspective, one of the most valuable concepts within BI is the “knowledge discovery in databases” or “data mining,” defined as “the process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (SPSS, 1997).

scholarly journals Decision support system for promotion of Faculty of Business and Economics Mendel University

2012 ◽  
Vol 60 (2) ◽  
pp. 443-448
Author(s):  
Pavel Turčínek ◽  
Arnošt Motyčka
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Secondary School ◽  
Knowledge Discovery ◽  
Business Intelligence ◽  
Knowledge Discovery In Databases ◽  
Optimum Number ◽  
Product Knowledge ◽  
Study Programs ◽  
Decision Making Processes

Decreasing number of secondary school graduates means that, for college, it becomes more difficult to fulfill guide number of newly admitted students. In order to maintain an optimum number of registered students, the Faculty of Business and Economics decided to support activities which increase the interest of its accredited programs.Potential students should be treated as customers to whom we want to offer a product – knowledge, skills and competencies. Promoting study programs PEF MENDELU is handled by PR department in collaboration with several students.Availability of resources for promotion is limited. It is crucial to deciding how to deal with these sources. By creating a system for monitoring and decision support, we provide all interested collaborators tool to improve decision-making processes.The system itself will be built on the tools of Business Intelligence (BI) that can observe consumer trends, identify customer segments and other important information. The BI emphasizes the use of OLAP technology for data processing. In the collected data about students is hidden a large amount of information that can be obtained using techniques such as knowledge discovery in databases.This article aims to describe the methodology for solving problems and show the application, which result in support of decision-making processes in the propagation PEF MENDELU, which should also lead to the efficiency of spending on this activity.

An Application of Binary Decision Trees to Pattern Recognition

2006 ◽  
Vol 10 (5) ◽  
pp. 682-687 ◽  
Author(s):  
Noboru Takagi ◽  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Pattern Recognition ◽  
Knowledge Discovery ◽  
Character Recognition ◽  
Decision Rules ◽  
Feature Space ◽  
Knowledge Discovery In Databases ◽  
Binary Decision ◽  
Japanese Character

Decision rules are a key technique in decision making, data mining and knowledge discovery in databases. We introduce an application of decision rules, handwriting pattern classification. When decision rules are applied to pattern recognition, one rule forms a hyperrectangle in feature space, i.e., each decision rule corresponds to one hyperrectangle. This means that a set of decision rules is considered a classification system, called the subclass method. We apply decision rules to handwritten Japanese character recognition, showing experimental results.

scholarly journals KNOWLEDGE DISCOVERY IN DATABASES FOR THE SUPPORT TO DECISION-MAKING IN NURSING-RELATED ACTIVITIES

2021 ◽  
Author(s):  
Ana Paula Sartorelli ◽  
Carlyne Lopata ◽  
Denilsen Carvalho Gomes ◽  
Marcia Regina Cubas ◽  
Deborah Ribeiro Carvalho
Keyword(s):  
Decision Making ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

ESTIMATING DBLEARN'S POTENTIAL FOR KNOWLEDGE DISCOVERY IN DATABASES

1995 ◽  
Vol 11 (2) ◽  
pp. 280-296 ◽  
Author(s):  
Howard J. Hamilton ◽  
David R. Fudger
Keyword(s):  
Knowledge Discovery ◽  
Knowledge Discovery In Databases
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