scholarly journals College students’ Network behavior Using data mining and feature analysis

2021 ◽  
Vol 2089 (1) ◽  
pp. 012075
Author(s):  
Thatiparthi Sravani ◽  
Srinivasa Rao Madala ◽  
Sk HeenaKauser
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Academic Advising ◽  
Network Activity ◽  
Feature Analysis ◽  
Network Behavior ◽  
Specific Level ◽  
Web Access ◽  
Advanced Analytics ◽  
Using Data

Abstract Teachers may use advanced analytics to rapidly and correctly understand undergraduate behavior trends, especially when it comes to identifying undergraduate groupings that need to be focused on at a later time. This study uses data mining cluster analysis to analyze the constituent behavior of 3,245 undergraduates in a specific level ‘B’ institution’s college network. According to the data, there are four different undergraduate groups with different Web access features, with 350 participants using the accomplishments and other variables of their success have an influence on these students. As a result of this research, we were able to collect data on undergraduate college network activity, which may be used to aid in the development of academic advising management.

Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty

2010 ◽  
Vol 37 (7) ◽  
pp. 5259-5264 ◽  
Author(s):  
Seyed Mohammad Seyed Hosseini ◽  
Anahita Maleki ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Customer Loyalty ◽  
Data Mining Approach ◽  
Using Data

Aid of End-Milling Condition Decision Using Data Mining from Tool Catalog Data for Rough Processing

2011 ◽  
Vol 325 ◽  
pp. 345-350
Author(s):  
Hiroyuki Kodama ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
Keiji Ogawa
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
End Milling ◽  
Hierarchical Cluster ◽  
Cutting Conditions ◽  
New Knowledge ◽  
End Mills ◽  
Milling Condition ◽  
Mining Methods ◽  
Using Data

The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing).

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.

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