Attribute-value distribution as a technique for increasing the efficiency of data mining.

2011 ◽  
pp. 46-63
Author(s):  
D. McSherry
Keyword(s):  
Data Mining ◽  
Value Distribution ◽  
Attribute Value

scholarly journals Multiple Data Source Discovery with Group Interaction Approach

2013 ◽  
Vol 760-762 ◽  
pp. 2141-2145
Author(s):  
Wu Hao
Keyword(s):  
Data Mining ◽  
Group Interaction ◽  
Attribute Level ◽  
Group Interactions ◽  
Multiple Data Source ◽  
Multiple Data ◽  
Medical Researchers ◽  
Data Source ◽  
Interaction Approach ◽  
Attribute Value

Medical researchers seek to identify and predict profit (or effectiveness) potential in a new medicine B against a specified disease by comparing it to an existing medicine A, which has been used to treat the disease for many years, called medicine assessment. Applying traditional data mining techniques to the medicine assessment, one can discover patterns, such as A.X=a à B.Y=b, which are identified at the attribute-value level. These patterns are useful in predicting associated behaviors at the attribute-value level. However, to evaluate B against A, we have to obtain globally useful relations between B and A at an attribute level. Therefore, this paper proposes a group interaction approach for multiple data source discovery. Group interactions include, such as rules, differences, and links between datasets. These group interactions are discovered at the attribute level. For example, R(A.X, B.Y), where R is a relationship, or a predication. Some examples are presented for illustrating the use of the group interaction approach.

scholarly journals Construction of an Attribute-Value Representation for Semi-structured Medical Findings Knowledge Extraction

2008 ◽  
Vol 11 (2) ◽  
Author(s):  
Daniel de Faveri Honorato ◽  
Everton Alvares Cherman ◽  
Huei Diana Lee ◽  
Maria Carolina Monard ◽  
Feng Chung Wu
Keyword(s):  
Data Mining ◽  
Natural Language ◽  
Knowledge Extraction ◽  
Automatic Process ◽  
Value Representation ◽  
Attribute Value

Data Mining is a process related to analysis, understanding and knowledge extraction from databases. In order to perform this process it is usually necessary to represent the data in the so called attribute-value format. This work proposes an extension of a methodology which supports, through a semi-automatic process, the construction of a table in the attribute-value format from information contained in medical findings which are described in natural language (Portuguese). A case study in which the methodology has been applied to a collection of Upper Digestive Endoscopies’ medical findings is presented. Results show the suitability of our proposal.

A LOGICAL FORMULATION OF ROUGH SET DEFINABILITY

2011 ◽  
Vol 19 (01) ◽  
pp. 27-37 ◽  
Author(s):  
TUAN-FANG FAN ◽  
CHURN-JUNG LIAU ◽  
DUEN-REN LIU
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Classical Logic ◽  
Propositional Logic ◽  
Rough Set Theory ◽  
First Order ◽  
Decision Logic ◽  
Data Tables ◽  
Attribute Value

In data mining problems, data is usually provided in the form of data tables. To represent knowledge discovered from data tables, a decision logic (DL) is proposed in rough set theory. DL is an instance of propositional logic, but we can use other logical formalisms to describe data tables. In this paper, we propose two descriptions of data tables based on first-order data logic (FODL) and attribute value-sorted logic (AVSL) respectively. In the context of FODL, we show that explicit definability and implicit definability in classical logic implies the notion of definability in rough set theory. We also show that AVSL is particularly useful for the representation of properties of many-valued data tables.

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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