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| 003 | EG-GiCUC | ||
| 005 | 20250223031038.0 | ||
| 008 | 140910s2013 ua d f m 000 0 eng d | ||
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_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aM.Sc | ||
| 099 | _aCai01.20.04.M.Sc.2013.Ay.O | ||
| 100 | 0 | _aAyman Taha Awad Allah Mohammed Farahat | |
| 245 | 1 | 0 |
_aOutlier values identification in data mining applications / _cAyman Taha Awad Allah Mohammed Farahat ; Supervised Osman M. Hegazy , Ali S. Hadi , Kareem M. Darwish |
| 246 | 1 | 5 | _aاكتــشاف القيــم الغيـــر نمطيـة فى تطبيقات التنقيب فى البيانات |
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_aCairo : _bAyman Taha Awad Allah Mohammed Farahat , _c2013 |
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| 300 |
_a194 Leaves : _bcharts ; _c30cm |
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| 502 | _aThesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Systems | ||
| 520 | _aOutliers 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 | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aData mining | |
| 653 | 4 | _aMeasuring association | |
| 653 | 4 | _aOut lievs detection categorical data | |
| 700 | 0 |
_aAli S. Hadi , _eSupervisor |
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| 700 | 0 |
_aKareem Mohamed Darwish , _eSupervisor |
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| 700 | 0 |
_aOsman Mohamed Hegazy , _eSupervisor |
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| 856 | _uhttp://172.23.153.220/th.pdf | ||
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_aAml _eCataloger |
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| 905 |
_aNazla _eRevisor |
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_2ddc _cTH |
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_c47288 _d47288 |
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