Characteristics Analysis of Heroin Abusers Based on Data Mining

2021 ◽  
pp. 1899-1906
Author(s):  
Ziyu Gong ◽  
Ning Zhu ◽  
Chunli Wang ◽  
Zongyu Liu ◽  
Yan Huang
Keyword(s):  
Data Mining ◽  
Heroin Abusers ◽  
Characteristics Analysis

The Application of Data Mining in the Community Characteristics Analysis of University Students

2014 ◽  
Vol 1008-1009 ◽  
pp. 1530-1535
Author(s):  
Xu Feng Zhang ◽  
Zi Min Wu ◽  
Qiu Chao Deng ◽  
Li Qiu Song
Keyword(s):  
Data Mining ◽  
Psychological Health ◽  
Family Income ◽  
Entrance Examination ◽  
College Entrance Examination ◽  
Tree Model ◽  
College Entrance ◽  
Clustering Model ◽  
Academic Management ◽  
Characteristics Analysis

This paper made a in-depth research on the application of data mining in academic management system based on overall understanding of data mining, and selected the data sample of students in grade 2007 of Logistics Management major of Logistics School of Beijing Wuzi University, this paper used Clementine software to establish decision tree model and clustering model to analyze students’ cluster characteristics by taking students’ academic achievement, score of college entrance examination, CET 4 and CET 6 level, students’ family income and psychological health, the research can help school notice development features of students in order to improve way of students’ cultivation.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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