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Classification of data mining techniques in intrusion detection: Classification Techniques in Data Mining - Brossura
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Data mining is the process of gathering, searching, and analyzing a large amount of raw data, as to discover patterns, relationships and behavior of data. There are large numbers of algorithms for classification of data mining. Single algorithm is not efficient for classification of data and recognize their pattern and behavior .There is a key term known as ensemble learning which means Combining two or more classifiers for efficient result. I have used the KDD’99 dataset for the experiment which have 41 features labeled either as normal or as an attack. In this book I have represented how graphical machine learning 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 and Boosting ensemble learning approach which are complementary naïve 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 naïve Bayes. However, the rule based classifiers become more efficient with bagging and boosting techniques.
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Informazioni sull?autore
Gaurav Mishra is born in India and has received His Bachelor’s degree in Computer Science & Engg.From Malout Institute of Management & Information Technology, Malout India. His areas of interest are Network Security, Java programming & Data Mining.
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Classification of data mining techniques in intrusion detection
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LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659442151
ISBN 13: 9783659442155
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Classification of data mining techniques in intrusion detection
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LAP LAMBERT Academic Publishing Aug 2013, 2013
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ISBN 13: 9783659442155
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data mining is the process of gathering, searching, and analyzing a large amount of raw data, as to discover patterns, relationships and behavior of data. There are large numbers of algorithms for classification of data mining. Single algorithm is not efficient for classification of data and recognize their pattern and behavior .There is a key term known as ensemble learning which means Combining two or more classifiers for efficient result. I have used the KDD'99 dataset for the experiment which have 41 features labeled either as normal or as an attack. In this book I have represented how graphical machine learning 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 and Boosting ensemble learning approach which are complementary naïve 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 naïve Bayes. However, the rule based classifiers become more efficient with bagging and boosting techniques. 52 pp. Englisch. Codice articolo 9783659442155
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Classification of data mining techniques in intrusion detection : Classification Techniques in Data Mining
Editore:
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659442151
ISBN 13: 9783659442155
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data mining is the process of gathering, searching, and analyzing a large amount of raw data, as to discover patterns, relationships and behavior of data. There are large numbers of algorithms for classification of data mining. Single algorithm is not efficient for classification of data and recognize their pattern and behavior .There is a key term known as ensemble learning which means Combining two or more classifiers for efficient result. I have used the KDD'99 dataset for the experiment which have 41 features labeled either as normal or as an attack. In this book I have represented how graphical machine learning 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 and Boosting ensemble learning approach which are complementary naïve 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 naïve Bayes. However, the rule based classifiers become more efficient with bagging and boosting techniques. Codice articolo 9783659442155
Quantità: 1 disponibili
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Classification of data mining techniques in intrusion detection
Editore:
LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10: 3659442151
ISBN 13: 9783659442155
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Taschenbuch. Condizione: Neu. Neuware -Data mining is the process of gathering, searching, and analyzing a large amount of raw data, as to discover patterns, relationships and behavior of data. There are large numbers of algorithms for classification of data mining. Single algorithm is not efficient for classification of data and recognize their pattern and behavior .There is a key term known as ensemble learning which means Combining two or more classifiers for efficient result. I have used the KDD¿99 dataset for the experiment which have 41 features labeled either as normal or as an attack. In this book I have represented how graphical machine learning 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 and Boosting ensemble learning approach which are complementary naïve 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 naïve Bayes. However, the rule based classifiers become more efficient with bagging and boosting techniques.Books on Demand GmbH, Überseering 33, 22297 Hamburg 52 pp. Englisch. Codice articolo 9783659442155
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Classification Of Data Mining Techniques In Intrusion Detection: Classification Techniques In Data Mining
Editore:
Lap Lambert Academic Publishing, 2013
ISBN 10: 3659442151
ISBN 13: 9783659442155
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Da: Revaluation Books, Exeter, Regno Unito
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Paperback. Condizione: Brand New. 52 pages. 8.66x5.91x0.12 inches. In Stock. Codice articolo __3659442151
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Foto dell'editore
Classification Of Data Mining Techniques In Intrusion Detection: Classification Techniques In Data Mining
Editore:
Lap Lambert Academic Publishing, 2013
ISBN 10: 3659442151
ISBN 13: 9783659442155
Nuovo
Paperback
Da: Revaluation Books, Exeter, Regno Unito
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: Brand New. 52 pages. 8.66x5.91x0.12 inches. In Stock. Codice articolo 3659442151
Quantità: 1 disponibili