Research and Application of Data Mining(DM) in the Analysis of Postgraduate Admission Information

2014 ◽  
Vol 687-691 ◽  
pp. 1254-1257
Author(s):  
Hui Hui
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Empirical Research ◽  
Association Analysis ◽  
Strong Support ◽  
Admission Data ◽  
Knowledge Theory ◽  
And Cluster Analysis

By applying the DM technologies such as Association analysis and Cluster analysis, this paper has made systematic empirical research combined with the postgraduate admission data of the key College C in Beijing, and also made description and analysis of the mining results. This paper has applied the DM method and knowledge theory in practice, which has offered strong support for the postgraduate admission management of College C.

Group Buy Website Studies Based on Factor Analysis and Cluster Analysis

2013 ◽  
Vol 321-324 ◽  
pp. 2995-2998
Author(s):  
Yun Jiang ◽  
Chong Wang ◽  
Dong Chen
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Major Group ◽  
Mining Methods ◽  
And Cluster Analysis

By collecting the major group buy websites data, this paper using factor analysis and cluster analysis in data mining methods to analysis it, classify the group buy website, find and analysis the group buy website operating key strategy.

scholarly journals Preparation and Cluster Analysis of Data from the Industrial Production Process for Failure Prediction

2016 ◽  
Vol 24 (39) ◽  
pp. 111-116 ◽  
Author(s):  
Martin Németh ◽  
German Michaľčonok
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Process Control ◽  
Production Process ◽  
Control Systems ◽  
Failure Prediction ◽  
Data Mining Algorithms ◽  
Failure Data ◽  
Process Control Systems ◽  
And Cluster Analysis

Abstract This article is devoted to the initial phase of data analysis of failure data from process control systems. Failure data can be used for example to detect weak spots in a production process, but also for failure prediction. To achieve these goals data mining techniques can be used. In this article, we propose a method to prepare and transform failure data from process control systems for application of data mining algorithms, especially cluster analysis.

scholarly journals SELECTION OF METRIC AND CATEGORICAL ATTRIBUTES OF RARE ANOMALOUS EVENTS IN A COMPUTER SYSTEM USING DATA MINING METHODS

T-Comm ◽  
2021 ◽  
Vol 15 (6) ◽  
pp. 40-47
Author(s):  
Oleg I. Sheluhin ◽  
◽  
Dmitry I. Rakovsky ◽  
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Cluster Analysis ◽  
Current Time ◽  
Trend Component ◽  
Zero Values ◽  
Categorical Attributes ◽  
Using Data ◽  
And Cluster Analysis ◽  
Selection Of

The process of marking multi-attribute experimental data for subsequent use by means of data mining in problems of detection and classification of rare anomalous events of computer systems (CS) is considered. The labeling process is carried out using three methods: manual preprocessing, statistical analysis and cluster analysis. Among the attributes of the metric type, the authors identified two macrogroups: “integral attributes” and “impulse attributes”. It is shown that the combination of statistical and cluster analysis methods increases the accuracy of detecting anomalous events in the CS, and also allows the selection of attributes according to their information significance. The expediency of manual preprocessing of data before clustering is shown by the example of dividing attributes into macrogroups, analyzing the density distribution using violin plot and removing the trend component using the method difference stationary series. With the help of construction of violin diagrams (Violin plot) for the attribute of the “integral” macrogroup, the distribution of states of the CS is shown. It is shown that the removal of the trend component by the DS-series method, normalization and reduction to absolute values allows more accurate marking of anomalous outliers, but this is not always acceptable. The interpretation of the clustering results performed for each normalized attribute shows that the normal values for all attributes are concentrated around zero values. The result of labeling experimental data is attribute-labeled data, where each attribute at the current time is assigned one of two states: abnormal or normal.

Assessment of retirement benefits in the European countries

2017 ◽  
Vol 62 (6) ◽  
pp. 23-37
Author(s):  
Alicja Jajko-Siwek
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Standard Of Living ◽  
Pension Scheme ◽  
Retirement Age ◽  
Economic Factors ◽  
Household Type ◽  
Retirement Benefits ◽  
Mining Methods ◽  
And Cluster Analysis

The aim of the research presented in the article is to characterize retired persons who receive benefits ensuring the maintenance of existing living conditions. The research was conducted with the use of selected data mining methods, such as classification trees, multivariate correspondence and cluster analysis. The paper includes socio-demographic and economic factors, i.e. sex, household type, retirement age, health status and type of pension scheme. The research was conducted on the basis of data from the project ”Share 50+ in Europe”. The presented results allow to identify beneficiary who is not threatened by the so-called pension gap which means the inability to maintain an earlier standard of living due to insufficient retirement benefits.

The limit of the contemporary financial system in Russia

2020 ◽  
Vol 16 (7) ◽  
pp. 1223-1245
Author(s):  
V.V. Smirnov
Keyword(s):  
Cluster Analysis ◽  
Foreign Currency ◽  
Financial System ◽  
Descriptive Statistics ◽  
High Tech ◽  
Monetary Aggregate ◽  
Internal System ◽  
Monetary Relations ◽  
And Cluster Analysis ◽  
Structural Preservation

Subject. The article focuses on the modern financial system of Russia. Objectives. I determine the limit of the contemporary financial system in Russia. Methods. The study is based on methods of descriptive statistics, statistical and cluster analysis. Results. The article shows the possibility of determining the scope of the contemporary financial system in Russia by establishing monetary relations as the order of the internal system and concerted operation of subsystems, preserving the structure of the financial system, maintaining the operational regime, implementing the program and achieving the goal. I found that the Russian financial system correlated with the Angolan one, and the real scope of the contemporary financial system in Russia. Conclusions and Relevance. As an attempt to effectively establish monetary relations and manage them, the limit of the contemporary financial system is related to the possibility of using Monetary Aggregate M0 to maintain the balance of the Central Bank of Russia. To overcome the scope of Russia’s financial system, the economy should have changed its specialization, refocusing it on high-tech export and increasing the foreign currency reserves. This can be done if amendments to Russia’s Constitution are adopted. The findings expand the scope of knowledge and create new competence in the establishment of monetary relations, order of the internal system and concerted interaction of subsystems, structural preservation of the financial system and maintenance of its operational regime.

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