Improved Data Mining Algorithms Based on an Early Warning System of College Students

2013 ◽  
Vol 8 (9) ◽  
Author(s):  
Lijuan Zhou ◽  
Yuyan Chen ◽  
Shuang Li
Keyword(s):  
College Students ◽  
Data Mining ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Early Warning System for SMEs as a Financial Risk Detector

2009 ◽  
pp. 220-238 ◽  
Author(s):  
Ali Serhan Koyuncugil
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Quantitative Data ◽  
Financial Risk ◽  
Warning System ◽  
Theoretical Background ◽  
Effect Level

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.

scholarly journals RESEARCH ON EARLY WARNING SYSTEM OF COLLEGE STUDENTS’ BEHAVIOR BASED ON BIG DATA ENVIRONMENT

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 659-665
Author(s):  
C. Y. Yang ◽  
J. Y. Liu ◽  
S. Huang
Keyword(s):  
College Students ◽  
Big Data ◽  
Early Warning ◽  
Behavioral Problems ◽  
Early Warning System ◽  
Warning System ◽  
Safety Control ◽  
Education Management ◽  
Data Environment ◽  
Behavior Based

Abstract. Because most schools have been using traditional methods to manage students, there is a lack of effective monitoring of students' behavioral problems. In order to solve this problem, this paper analyses the characteristics of big data in University campus, adopts K-Means algorithm, a traditional clustering analysis algorithm, and proposes an early warning system of College Students' behavior based on Internet of Things and big data environment under the mainstream Hadoop open source platform. The system excavates and analyses the potential connections in the massive data of these campuses, studies the characteristics of students' behavior, analyses the law of students' behavior, and clusters the categories of students' behavior. It can provide students, colleges, schools and logistics management departments with multi-dimensional behavior analysis and prediction, early warning and safety control of students' behavior, realize the informatization of students' management means, improve the scientific level of students' education management, and promote the construction of intelligent digital campus.

Data Mining Techniques to Improve Early Warning Systems across the Bay of Bengal

2013 ◽  
pp. 1349-1383
Author(s):  
Hakikur Rahman
Keyword(s):  
Data Mining ◽  
Early Warning ◽  
Bay Of Bengal ◽  
Early Warning System ◽  
Ethical Issues ◽  
Warning System ◽  
Major Portion ◽  
Data Mining Techniques ◽  
Landing Zone ◽  
Bangladeshi Population

This chapter is a conceptual contribution to this book on data mining applications upholding ethical issues related to two extremely important aspects of the Bangladeshi population: the early warning system and the disaster management system. The chapter tries to provide a few conceptual ideas to introduce ethical data mining application in these systems to support the agencies that are involved for an improved, efficient, and transparent support system in the country, especially across the Bay of Bengal. Resembling a triangular shape (deltaic), a major portion of the bay touches the southern portion of Bangladesh. Sediments from rivers have made the bay a shallow sea. Due to its shallowness and shape, monsoon rains and cyclone storms become destructive, causing great loss of life along the southern part of the country. Moreover, the three mighty rivers (Padma, Jamuna, and Meghna) form one of the largest river systems in the world. They have a large number of distributaries and tributaries, which cause a major portion of the country to be inundated by monsoon rain. In addition, being the lowest landing zone of the Himalayan water, Bangladesh becomes victim to floods almost every year. Loss of lives, destruction of properties, suffering of numerous people and hampering of economic development have become part and parcel of Bangladeshi communities. This chapter suggests that the newly emerged data mining techniques can be introduced to collect, synthesize, analyze, archive, disseminate, and even make future forecasts forming a reliable early warning system across the Bay of Bengal.

Dynamic early warning system of college students’ target course performance based on improved Apriori algorithm

2021 ◽  
pp. 1-17
Author(s):  
Guo Hao ◽  
Shaocui Guo ◽  
Guohong Sun ◽  
Wenbo Yang
Keyword(s):  
College Students ◽  
Early Warning ◽  
Educational Administration ◽  
Early Warning System ◽  
Warning System ◽  
Colleges And Universities ◽  
Apriori Algorithm ◽  
Course Performance ◽  
Course Achievement ◽  
Performance System

The early warning system of College Students’ target course achievement is an important part of the educational administration system in Colleges and universities. This paper proposes to use some techniques of association principle to mine a large amount of data in the performance system to a certain extent, and obtain available rules from the data. Based on the characteristics and shortcomings of Apriori algorithm, an improved Apriori is proposed. The algorithm can process and mine the data in the early warning system of College Students’ scores, and finally obtain the management principles, thus forming an effective early warning for the course learning. In order to promote the improvement of students’ academic performance and achieve the ultimate goal of cultivating excellent talents in Colleges and universities.

Data Mining Techniques to Improve Early Warning Systems across the Bay of Bengal

2013 ◽  
pp. 53-86 ◽  
Author(s):  
Hakikur Rahman
Keyword(s):  
Data Mining ◽  
Early Warning ◽  
Bay Of Bengal ◽  
Early Warning System ◽  
Ethical Issues ◽  
Warning System ◽  
Major Portion ◽  
Data Mining Techniques ◽  
Landing Zone ◽  
Bangladeshi Population

This chapter is a conceptual contribution to this book on data mining applications upholding ethical issues related to two extremely important aspects of the Bangladeshi population: the early warning system and the disaster management system. The chapter tries to provide a few conceptual ideas to introduce ethical data mining application in these systems to support the agencies that are involved for an improved, efficient, and transparent support system in the country, especially across the Bay of Bengal. Resembling a triangular shape (deltaic), a major portion of the bay touches the southern portion of Bangladesh. Sediments from rivers have made the bay a shallow sea. Due to its shallowness and shape, monsoon rains and cyclone storms become destructive, causing great loss of life along the southern part of the country. Moreover, the three mighty rivers (Padma, Jamuna, and Meghna) form one of the largest river systems in the world. They have a large number of distributaries and tributaries, which cause a major portion of the country to be inundated by monsoon rain. In addition, being the lowest landing zone of the Himalayan water, Bangladesh becomes victim to floods almost every year. Loss of lives, destruction of properties, suffering of numerous people and hampering of economic development have become part and parcel of Bangladeshi communities. This chapter suggests that the newly emerged data mining techniques can be introduced to collect, synthesize, analyze, archive, disseminate, and even make future forecasts forming a reliable early warning system across the Bay of Bengal.

Early Warning System Framework Proposal Based on Structured Analytical Techniques, SNA, and Fuzzy Expert System for Different Industries

Fuzzy Systems ◽  
2017 ◽  
pp. 202-234
Author(s):  
Goran Klepac ◽  
Robert Kopal ◽  
Leo Mrsic
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Fuzzy Expert System ◽  
Warning Systems ◽  
Single Data ◽  
Set Up

Early warning systems are made with purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible and with significant relevance. There are numerous ways to set up early warning systems within company. Those solutions are often based on single data mining methods, and they rarely provide the holistic and qualitative approach needed in modern market uncertainty conditions. This chapter gives a novel concept for early warning system design within company, applicable in different industries. The core of the proposed framework is hybrid fuzzy expert system, which can contain a variety of data mining predictive models responsible for some specific areas in addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. As part of this framework, SNA methods are also explained and introduced as a very powerful and unique tool to be used in modern early warning systems.

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