Frequent Closures as a Concise Representation for Binary Data Mining

2000 ◽  
pp. 62-73 ◽  
Author(s):  
Jean-François Boulicaut ◽  
Artur Bykowski
Keyword(s):  
Data Mining ◽  
Binary Data ◽  
Concise Representation

Logical Analysis of Data

2011 ◽  
pp. 689-692 ◽  
Author(s):  
Endre Boros ◽  
Peter L. Hammer ◽  
Toshihide Ibaraki
Keyword(s):  
Data Mining ◽  
Boolean Function ◽  
Categorical Data ◽  
Boolean Functions ◽  
Binary Data ◽  
Logical Form ◽  
Logical Analysis ◽  
Logical Analysis Of Data ◽  
Primary Target ◽  
The Given

The logical analysis of data (LAD) is a methodology aimed at extracting or discovering knowledge from data in logical form. The first paper in this area was published as Crama, Hammer, & Ibaraki (1988) and precedes most of the data mining papers appearing in the 1990s. Its primary target is a set of binary data belonging to two classes for which a Boolean function that classifies the data into two classes is built. In other words, the extracted knowledge is embodied as a Boolean function, which then will be used to classify unknown data. As Boolean functions that classify the given data into two classes are not unique, there are various methodologies investigated in LAD to obtain compact and meaningful functions. As will be mentioned later, numerical and categorical data also can be handled, and more than two classes can be represented by combining more than one Boolean function.

Information Mining

2010 ◽  
pp. 223-231
Author(s):  
Manjunath Ramachandra
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Supply Chain ◽  
Binary Data ◽  
Information Mining

The data in its raw form may not be of much use for the end customer. In the attempt to extract the knowledge from the data, the concept of data mining is extremely useful. This chapter explains how the data is to be filtered out to extract useful information. Often, exactly this information is requested by the players of the supply chain towards decision making. They are not interested in the binary data.

scholarly journals An Extension of Totohasina’s Normalization Theory of Quality Measures of Association Rules

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Armand ◽  
André Totohasina ◽  
Daniel Rajaonasy Feno
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Binary Data ◽  
Quality Measures ◽  
Normalization Method ◽  
Measures Of Association ◽  
Interestingness Measures ◽  
Affine Homeomorphism

In the context of binary data mining, for unifying view on probabilistic quality measures of association rules, Totohasina’s theory of normalization of quality measures of association rules primarily based on affine homeomorphism presents some drawbacks. Indeed, it cannot normalize some interestingness measures which are explained below. This paper presents an extension of it, as a new normalization method based on proper homographic homeomorphism that appears most consequent.

scholarly journals Generating Non-redundant Multilevel Association Rules Using Min-max Exact Rules

2018 ◽  
Vol 8 (6) ◽  
pp. 4568
Author(s):  
R. Vijaya Prakash ◽  
S. S. V. N. Sarma ◽  
M. Sheshikala
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Huge Number ◽  
Concise Representation ◽  
Multi Level ◽  
The Given ◽  
Multilevel Association Rules

Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.

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.

The Beck Hopelessness Scale

2016 ◽  
Vol 32 (2) ◽  
pp. 111-118 ◽  
Author(s):  
Marianna Szabó ◽  
Veronika Mészáros ◽  
Judit Sallay ◽  
Gyöngyi Ajtay ◽  
Viktor Boross ◽  
...  
Keyword(s):  
Binary Data ◽  
Factor Model ◽  
Clinical Sample ◽  
Analytical Techniques ◽  
Cross Cultural ◽  
Cultural Validity ◽  
Beck Hopelessness Scale ◽  
Method Effects ◽  
Hungarian Population ◽  
Components Analysis

Abstract. The aim of the present study was to examine the construct and cross-cultural validity of the Beck Hopelessness Scale (BHS; Beck, Weissman, Lester, & Trexler, 1974 ). Beck et al. applied exploratory Principal Components Analysis and argued that the scale measured three specific components (affective, motivational, and cognitive). Subsequent studies identified one, two, three, or more factors, highlighting a lack of clarity regarding the scale’s construct validity. In a large clinical sample, we tested the original three-factor model and explored alternative models using both confirmatory and exploratory factor analytical techniques appropriate for analyzing binary data. In doing so, we investigated whether method variance needs to be taken into account in understanding the structure of the BHS. Our findings supported a bifactor model that explicitly included method effects. We concluded that the BHS measures a single underlying construct of hopelessness, and that an incorporation of method effects consolidates previous findings where positively and negatively worded items loaded on separate factors. Our study further contributes to establishing the cross-cultural validity of this instrument by showing that BHS scores differentiate between depressed, anxious, and nonclinical groups in a Hungarian population.

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