How Chaos Theory improves data mining in research by means of ALEV

In this paper a method for data reduction is introduced. Aspects of Lyapunov, entropy and variance (ALEV) provide an approach for mining large stocks of time series data. Methods of artificial intelligence (AI) offer two different ways for modeling observation data: the recall times of expert system...

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Bibliographic Details
Published in:2008 Canadian Conference on Electrical and Computer Engineering pp. 000703 - 000708
Main Author: Toplak, W.
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2008
Subjects:
Artificial intelligence
Chaos
Chaos Theory
Computing Algorithms
Data mining
Database and Data Mining
Entropy
Expert systems
Humans
Information systems
Neural networks
Performance Evaluation
Road vehicles
Telecommunication traffic
ISBN:9781424416424, 1424416426
ISSN:0840-7789
Online Access:Get full text
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Summary:In this paper a method for data reduction is introduced. Aspects of Lyapunov, entropy and variance (ALEV) provide an approach for mining large stocks of time series data. Methods of artificial intelligence (AI) offer two different ways for modeling observation data: the recall times of expert systems (XPS) depend on the size of a knowledge base. Connectionist approaches like the multi-layer perceptron (MLP) have to be trained with a representative data set for mapping system behavior. While the duration of this learning process also depends on the amount of representative data the recall times are very short. On basis of the Mackey-Glass function a technique for visual data mining (VDM) is proposed. Performance tests on basis of real world traffic speed patterns from different observation time periods show that ALEV thins out large pattern stocks. Viability of data mining methods is increased and generalization quality remains the same.
ISBN:9781424416424
1424416426
ISSN:0840-7789
DOI:10.1109/CCECE.2008.4564626