9783848480135 - survey on data mining techniques in intrusion detection: information security with data mining de chauhan, amanpreet (6 resultados)
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Librería: preigu, Osnabrück, Alemaniapreigu
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Taschenbuch. Condición: Neu. Survey on data mining techniques in intrusion detection | Information Security with Data Mining | Amanpreet Chauhan | Taschenbuch | 64 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783848480135 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 O…snabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
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paperback. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data mining is the extraction of the valuable data from the large amount of data. There are numerous number of algorithms available for data mining. Single algorithm is not capable of producing efficient results. There is a key term… in data mining known as ensemble learning which means combining two or more classifiers for the better result.I have used the KDD'99 dataset for the experiment which have 41 features labeled either as normal or as attack. In this book I have represented how the graphical tool weka can be used for data mining and how ensemble learning can be implemented using weka. I have used three classifiers with the Bagging ensemble learning approach which are complementary naive bayes and two are rule based classifiers, part and jrip. My experiment shows that bagging improves the efficiency of the rule based classifiers as well as of naive bayes; however, the rule based classifiers become more efficient with bagging. 64 pp. Englisch.
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chauhan AmanpreetAmanpreet Chauhan is born in India and has received his Bachelor s degree in Computer Science & Engg. from Malout Institute of Management & Information Technology,Malout India.Currently… he is pursuing Advanced Diplo.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data mining is the extraction of the valuable data from the large amount of data. There are numerous number of algorithms available for data mining. Single algorithm is not capable of producing efficient results. There is a key term in…data mining known as ensemble learning which means combining two or more classifiers for the better result.I have used the KDD'99 dataset for the experiment which have 41 features labeled either as normal or as attack. In this book I have represented how the graphical tool weka can be used for data mining and how ensemble learning can be implemented using weka. I have used three classifiers with the Bagging ensemble learning approach which are complementary naive bayes and two are rule based classifiers, part and jrip. My experiment shows that bagging improves the efficiency of the rule based classifiers as well as of naive bayes; however, the rule based classifiers become more efficient with bagging.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data mining is the extraction of the valuable data from the large amount of data. There are numerous number of algorithms available for data mining. Single algorithm is not capable of producing efficient results. There is a key term in d…ata mining known as ensemble learning which means combining two or more classifiers for the better result.I have used the KDD'99 dataset for the experiment which have 41 features labeled either as normal or as attack. In this book I have represented how the graphical tool weka can be used for data mining and how ensemble learning can be implemented using weka. I have used three classifiers with the Bagging ensemble learning approach which are complementary naive bayes and two are rule based classifiers, part and jrip. My experiment shows that bagging improves the efficiency of the rule based classifiers as well as of naive bayes; however, the rule based classifiers become more efficient with bagging.
