Packaging Big Data Visualization Based on Computational Intelligence Information Design

A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an optimization method and simultaneously determines the...

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Bibliographic Details
Published in:Computational intelligence and neuroscience Vol. 2022; pp. 1 - 10
Main Author: Zhang, Guangchao
Format: Journal Article
Language:English
Published: United States Hindawi 23.04.2022
John Wiley & Sons, Inc
Subjects:
Algorithms
Artificial Intelligence
Big Data
Classification
Communication
Computer applications
Customization
Data Visualization
Design
Entropy
Information sharing
Information sources
Information storage
Information technology
Intelligence
Internet
Online data bases
Optimization
Overcrowding
Packaging
Product life cycle
Sampling
Scientific visualization
Society
User needs
Visualization
Visualization (Computers)
Weight reduction
China
ISSN:1687-5265, 1687-5273, 1687-5273
Online Access:Get full text
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Summary:A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an optimization method and simultaneously determines the appropriate stopping conditions to avoid overcrowding. The effectiveness of the improved algorithm has been experimentally proven. Methods of character reduction and weight determination are used to reduce the index and weight, establishing a large packaging information system. Experimental results show that the improved algorithm in this article produces more classification rules than the CAIM algorithm, because the discrete intervals created by the CAIM algorithm are relatively simple, but the classification rules are few, but less than the number of CAIM algorithms. Classification rules are generated by entropy-based sampling algorithms. This can make the classification rules simple and universal, and it is clear that the optimal sampling algorithm is more accurate than the CAIM algorithm.
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Academic Editor: Shakeel Ahmad
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2022/4558839