Data Bases, the Base for Data Mining

2009 ◽  
pp. 37-58 ◽  
Author(s):  
Christian Buchsbaum ◽  
Sabine Hãhler-Schlimm ◽  
Silke Rehme
Keyword(s):  
Data Mining ◽  
Data Bases

scholarly journals Data Mining of Historic Hydrogeological and Socioeconomic Data Bases of the Toluca Valley, Mexico

2016 ◽  
Vol 08 (04) ◽  
pp. 522-533
Author(s):  
Oliver López-Corona ◽  
Oscar Escolero Fuentes ◽  
Eric Morales-Casique ◽  
Pablo Padilla Longoria ◽  
Tomás González Moran
Keyword(s):  
Data Mining ◽  
Data Bases ◽  
Toluca Valley ◽  
Socioeconomic Data

Data Bases, the Base for Data Mining

2009 ◽  
pp. 37-58 ◽  
Author(s):  
Christian Buchsbaum ◽  
Sabine Hãhler-Schlimm ◽  
Silke Rehme
Keyword(s):  
Data Mining ◽  
Data Bases

Data Mining and Statistics

2017 ◽  
pp. 15-33
Author(s):  
Hirak Dasgupta
Keyword(s):  
Data Mining ◽  
Observational Data ◽  
Data Cleaning ◽  
Data Bases ◽  
The Past ◽  
The World ◽  
Analyse Data ◽  
Combination Of Methods ◽  
Age Of Information

In the age of information, the world abounds with data. In order to obtain an intelligent appreciation of current developments, we need to absorb and interpret substantial amounts of data. The amount of data collected has grown at a phenomenal rate over the past few years. The computer age has given us both the power to rapidly process, summarize and analyse data and the encouragement to produce and store more data. The aim of data mining is to make sense of large amounts of mostly unsupervised data, in some domain. Data Mining is used to discover the patterns and relationships in data, with an emphasis on large observational data bases. This chapter aims to compare the approaches and conclude that Statisticians and Data miners can profit by studying each other's methods by using the combination of methods judiciously. The chapter also attempts to discuss data cleaning techniques involved in data mining.

scholarly journals Colors of the Sublunar

i-Perception ◽  
2017 ◽  
Vol 8 (5) ◽  
pp. 204166951773348 ◽  
Author(s):  
Jan Koenderink ◽  
Andrea van Doorn
Keyword(s):  
Data Mining ◽  
Human Visual System ◽  
Image Data ◽  
Data Sources ◽  
Heuristic Methods ◽  
Blue Color ◽  
Data Bases ◽  
Terrestrial Environment ◽  
Typical Data ◽  
Image Transformations

Generic red, green, and blue images can be regarded as data sources of coarse (three bins) local spectra, typical data volumes are 104 to 107 spectra. Image data bases often yield hundreds or thousands of images, yielding data sources of 109 to 1010 spectra. There is usually no calibration, and there often are various nonlinear image transformations involved. However, we argue that sheer numbers make up for such ambiguity. We propose a model of spectral data mining that applies to the sublunar realm, spectra due to the scattering of daylight by objects from the generic terrestrial environment. The model involves colorimetry and ecological physics. Whereas the colorimetry is readily dealt with, one needs to handle the ecological physics with heuristic methods. The results suggest evolutionary causes of the human visual system. We also suggest effective methods to generate red, green, and blue color gamuts for various terrains.

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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