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A goal programming approach to fuzzy cluster analysis with missing data / Aliaa Hamza Sayed ; Supervised Ramadan Hamed Mohamed , Mahmoud Mostafa Rashwan , Hesham Abdelmeguid Abdalla

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Aliaa Hamza Sayed , 2017Description: 124 P. ; 25cmOther title:
  • أسلوب برمجة هدف للتحليل العنقود المشوش فى وجود بيانات مفقودة [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics Summary: Cluster analysis plays an important role in a wide spectrum of applications. In practice, some problems such as handling missing data, noise or outliers are often faced. So, the clustering problem becomes more challenging in the presence of such problems. This thesis addresses the problem of fuzzy cluster analysis with missing data as follows: Firstly, it presents methods of fuzzy cluster analysis-which were discussed in the literature-that can be extended to process datasets with missing values through incorporating them in the data analysis process rather than pre-processing. Secondly, it proposes a new approach for treating the problem of missing data in fuzzy cluster analysis based on goal programming
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.M.Sc.2017.Al.G (Browse shelf(Opens below)) Not for loan 01010110073046000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.M.Sc.2017.Al.G (Browse shelf(Opens below)) 73046.CD Not for loan 01020110073046000

Thesis (M.Sc.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics

Cluster analysis plays an important role in a wide spectrum of applications. In practice, some problems such as handling missing data, noise or outliers are often faced. So, the clustering problem becomes more challenging in the presence of such problems. This thesis addresses the problem of fuzzy cluster analysis with missing data as follows: Firstly, it presents methods of fuzzy cluster analysis-which were discussed in the literature-that can be extended to process datasets with missing values through incorporating them in the data analysis process rather than pre-processing. Secondly, it proposes a new approach for treating the problem of missing data in fuzzy cluster analysis based on goal programming

Issued also as CD

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