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...
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| Veröffentlicht in: | Mobile networks and applications Jg. 27; H. 4; S. 1359 - 1367 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
Springer US
01.08.2022
Springer Nature B.V |
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| ISSN: | 1383-469X, 1572-8153 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Wang, Haiqing |
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| Cites_doi | 10.1109/TITS.2019.2910548 10.1007/s11042-015-3148-6 10.3233/JIFS-17170 10.1109/TVLSI.2020.2995094 10.1016/j.ins.2019.01.046 10.1016/j.apm.2018.08.012 10.1007/s10489-017-1089-7 10.1109/TKDE.2018.2842191 10.1109/TCAD.2019.2946243 10.1007/s11222-016-9670-1 10.1016/j.neucom.2017.01.017 10.2478/AMNS.2019.1.00025 10.1109/TIP.2017.2789327 10.1016/j.ins.2020.02.051 10.1109/TAC.2018.2843294 10.1016/j.neucom.2018.11.091 10.2478/amns.2020.1.00044 10.1093/mnras/sty2690 10.1007/s10618-019-00635-1 10.1109/TLA.2018.8528242 10.2478/amns.2019.2.00035 10.1049/iet-ipr.2018.0043 10.3233/IDA-173488 10.1109/TNNLS.2019.2911118 10.1007/s00477-017-1441-9 10.1016/j.apnum.2020.03.003 10.1016/j.fss.2018.11.017 10.1155/2019/7875305 10.1109/TFUZZ.2020.3026140 10.1016/j.swevo.2020.100697 |
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| Keywords | Symmetric encryption algorithm Multi source data Data generalization method Baud interval equalization Association clustering |
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| SubjectTerms | 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 |
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| Title | Multi Source Data Association Clustering Analysis Based on Symmetric Encryption Algorithm |
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