scholarly journals The GUHA method and its meaning for data mining

2010 ◽  
Vol 76 (1) ◽  
pp. 34-48 ◽  
Author(s):  
Petr Hájek ◽  
Martin Holeňa ◽  
Jan Rauch
Keyword(s):  
Data Mining ◽  
Guha Method

scholarly journals Revealing Driver’s Natural Behavior—A GUHA Data Mining Approach

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1818
Author(s):  
Esko Turunen ◽  
Klara Dolos
Keyword(s):  
Data Mining ◽  
The Other ◽  
Vehicle Speed ◽  
Mining Method ◽  
Computer Implementation ◽  
Natural Behavior ◽  
Data Mining Method ◽  
Data Mining Approach ◽  
Vehicle Data ◽  
Guha Method

We investigate the applicability and usefulness of the GUHA data mining method and its computer implementation LISp-Miner for driver characterization based on digital vehicle data on gas pedal position, vehicle speed, and others. Three analytical questions are assessed: (1) Which measured features, also called attributes, distinguish each driver from all other drivers? (2) Comparing one driver separately in pairs with each of the other drivers, which are the most distinguishing attributes? (3) Comparing one driver separately in pairs with each of the other drivers, which attributes values show significant differences between drivers? The analyzed data consist of 94,380 measurements and contain clear and understandable patterns to be found by LISp-Miner. In conclusion, we find that the GUHA method is well suited for such tasks.

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.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

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