An improved fuzzy C-means clustering algorithm for assisted therapy of chronic bronchitis.
Background: Bronchitis is considered a non-specific inflammation in the peripheral tissues of the trachea and bronchus. Many therapeutic schemes for chronic bronchitis have been reported in existing research.Methods: This work attempted to conduct optimization analysis of the therapeutic scheme for...
| Publicado en: | Technology & Health Care Vol. 23; no. 6; pp. 699 - 714 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110815382&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110815382 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09287329 3QT jtl: Technology & Health Care issn: 09287329 maglogo: N pubinfo: dt: 2015 vid: 23 iid: 6 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 110815382 110815382 NLM26409511 110815382 10.3233/THC-151023 NLM26409511 110815382 ppf: 699 ppct: 15 formats: tig: atl: An improved fuzzy C-means clustering algorithm for assisted therapy of chronic bronchitis. aug: au: Weijia Lua Zhuangzhi Yan Lu, Weijia Yan, Zhuangzhi affil: School of Communication and Information Engineering, Shanghai University, Shanghai, China sug: subj: Logic Bronchitis, Chronic Therapy Clustering Algorithms Data Mining Methods Computer Simulation Human ab: Background: Bronchitis is considered a non-specific inflammation in the peripheral tissues of the trachea and bronchus. Many therapeutic schemes for chronic bronchitis have been reported in existing research.Methods: This work attempted to conduct optimization analysis of the therapeutic scheme for chronic bronchitis using a data mining method. To overcome the shortfalls of the current fuzzy C-means clustering (FCM) algorithm, this research proposed an improved kernel fuzzy C-means (KFCM) clustering algorithm. The improved KFCM algorithm solved traditional cluster algorithm problems in two ways: firstly FCM clustering was mapped in high-dimensional kernel space; and the samples in the initial input space R(S) were mapped to high-dimensional feature space R(p). Finally, the improved and traditional algorithms by computer simulation experiments.Results: Based on the analysis of the simulation experiments on IRIS dataset in this research, improved KFCM algorithm could improve calculation accuracy by 10% because the initial value greatly decreased the number of iterations and improved the accuracy of the calculation.Conclusion: The improved KFCM algorithm was used to optimize the relationship between data structures in the process of iteration clustering so as to accelerate iteration convergence. The simulation results show that the improved KFCM algorithm performs better in terms of both calculating performance and clustering correctness. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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