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Outlier values identification in data mining applications / Ayman Taha Awad Allah Mohammed Farahat ; Supervised Osman M. Hegazy , Ali S. Hadi , Kareem M. Darwish

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Ayman Taha Awad Allah Mohammed Farahat , 2013Description: 194 Leaves : charts ; 30cmOther title:
  • اكتــشاف القيــم الغيـــر نمطيـة فى تطبيقات التنقيب فى البيانات [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Systems Summary: Outliers identification algorithms for categorical data usually take long computational time.They also strongly depend on parameter settings that require prior information about the data,e.g.,number of outliers in the data, maximum length of itemsets and/or minimum support for frequent itemsets.These input parameters are classified into two groups;(a)in-trinsic parameters which are required by an outliers detection method to produce a score for each object and(b) decision parameters which are required to decide if anobject is an out-lier based on the score
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Item type Current library Home library Call number Copy number Status Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.04.M.Sc.2013.Ay.O (Browse shelf(Opens below)) Not for loan 01010110063306000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.04.M.Sc.2013.Ay.O (Browse shelf(Opens below)) 63306.CD Not for loan 01020110063306000

Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Systems

Outliers identification algorithms for categorical data usually take long computational time.They also strongly depend on parameter settings that require prior information about the data,e.g.,number of outliers in the data, maximum length of itemsets and/or minimum support for frequent itemsets.These input parameters are classified into two groups;(a)in-trinsic parameters which are required by an outliers detection method to produce a score for each object and(b) decision parameters which are required to decide if anobject is an out-lier based on the score

Issued also as CD

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