scholarly journals A Framework for Data Mining Pattern Management

2004 ◽  
pp. 87-98 ◽  
Author(s):  
Barbara Catania ◽  
Anna Maddalena ◽  
Maurizio Mazza ◽  
Elisa Bertino ◽  
Stefano Rizzi
Keyword(s):  
Data Mining ◽  
Mining Pattern

Application and Research of Data Mining in Micro Course Platform Construction

2014 ◽  
Vol 962-965 ◽  
pp. 3003-3006
Author(s):  
Bao Xian Jia
Keyword(s):  
Data Mining ◽  
Key Words ◽  
Personalized Recommendation ◽  
Basic Information ◽  
Personalized Service ◽  
Learning Content ◽  
Content Recommendation ◽  
Matching Degree ◽  
Access Information ◽  
Mining Pattern

Data mining can be used to make modeling for individual learner's usage record, combining with learner's basic information to make analysis of his habits, personal preferences to provide personalized service for the learner. At the same time by collecting and counting learners’ recent access information in micro course platform to analyse the learning content, compare and match with mining pattern, and to sort according to the matching degree, forecasting the most possible knowledge for the learner in the next step, attaching sorting result to the end of the learner’s requested page, for the learning content recommendation consequently, etc. Paper mainly introduced the specific application of data mining in micro course platform BBS. Key words: data mining, micro courses, personalized recommendation

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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