Data Mining as Generalization: A Formal Model

According to statistics, the number of samples of new attacks against information systems is increasing due to inability to detect new patterns and the lack of modern attack detection systems. To resolve this issue, we implement intelligent data analysis to detect attacks. There are many data mining programs, so it is important to choose the best program. The authors investigate the problem of information security from the viewpoint of new attacks, the programs for mining of information system’s events. The criteria for their evaluation have been formulated. Besides, the formal model for the study of programs for mining of information system’s events has been developed. The proposed formal model will help to choose the best program for event mining. If the requirements for the analyzed programs change, then changing the values in the best vector can also become a right solution. Thus, the developed formal evaluation model is universal and effective.

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Data Mining and Machine Learning

10.1017/9781108564175 ◽

2020 ◽

Cited By ~ 2

Author(s):

Mohammed J. Zaki ◽

Wagner Meira, Jr

Keyword(s):

Machine Learning ◽

Data Mining

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Proofs from Formal Model in Chapter 4

Delegating Powers ◽

10.1017/cbo9780511609312.012 ◽

1999 ◽

pp. 245-256

Keyword(s):

Formal Model

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The economics of selection of mail orders Drs. Zahavi and Levin are the masterminds behind the development of AMOS, a customized predictive modeling system for the Franklin Mint in Philadelphia, and GainSmarts, a general purpose data mining system that is the two-time winner of the KDD-CUP competition for the best data mining tools (1997 and 1998) sponsored by the American Association for Artificial Intelligence.

Journal of Interactive Marketing ◽

10.1002/dir.1016.abs ◽

2001 ◽

Vol 15 (3) ◽

pp. 53

Author(s):

Nissan Levin ◽

Jacob Zahavi

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Predictive Modeling ◽

American Association ◽

General Purpose ◽

Mining System ◽

Data Mining System ◽

Mining Tools ◽

Selection Of

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Heart Rate Variability, Emotions, and Music

Journal of Psychophysiology ◽

10.1027/0269-8803/a000021 ◽

2010 ◽

Vol 24 (2) ◽

pp. 112-119 ◽

Cited By ~ 9

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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