Multi Source Data Association Clustering Analysis Based on Symmetric Encryption Algorithm

Due to the low clustering accuracy of the existing methods, a multi-source data association clustering method based on symmetric encryption algorithm is proposed. The multi-source data acquisition model is established, and the baud interval equalization sampling method is adopted to acquire data. Th...

Full description

Saved in:
Bibliographic Details
Published in:Mobile networks and applications Vol. 27; no. 4; pp. 1359 - 1367
Main Author: Wang, Haiqing
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2022
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Classification
Cluster analysis
Clustering
Communications Engineering
Computer Communication Networks
Data acquisition
Data collection
Data transmission
Decision making
Electrical Engineering
Engineering
IT in Business
Methods
Networks
Spectrum allocation
Traffic congestion
Wavelet transforms
Symmetric encryption algorithm
Multi source data
Data generalization method
Baud interval equalization
Association clustering
ISSN:1383-469X, 1572-8153
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Due to the low clustering accuracy of the existing methods, a multi-source data association clustering method based on symmetric encryption algorithm is proposed. The multi-source data acquisition model is established, and the baud interval equalization sampling method is adopted to acquire data. The method of data generalization is applied to classify the collected data attributes optimally, which eliminates the fuzzy data. The symmetric encryption algorithm is used to shorten the range of data query. According to the centrosymmetry of data similarity measurement, multi-source data association clustering is realized. The proposed method can realize different types of data clustering, with high clustering accuracy and strong practical application value.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1383-469X
1572-8153
DOI:10.1007/s11036-022-01922-w