Outlier values identification in data mining applications /
Ayman Taha Awad Allah Mohammed Farahat
Outlier values identification in data mining applications / اكتــشاف القيــم الغيـــر نمطيـة فى تطبيقات التنقيب فى البيانات Ayman Taha Awad Allah Mohammed Farahat ; Supervised Osman M. Hegazy , Ali S. Hadi , Kareem M. Darwish - Cairo : Ayman Taha Awad Allah Mohammed Farahat , 2013 - 194 Leaves : charts ; 30cm
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
Data mining Measuring association Out lievs detection categorical data
Outlier values identification in data mining applications / اكتــشاف القيــم الغيـــر نمطيـة فى تطبيقات التنقيب فى البيانات Ayman Taha Awad Allah Mohammed Farahat ; Supervised Osman M. Hegazy , Ali S. Hadi , Kareem M. Darwish - Cairo : Ayman Taha Awad Allah Mohammed Farahat , 2013 - 194 Leaves : charts ; 30cm
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
Data mining Measuring association Out lievs detection categorical data