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Optimized sampling with clustering approach for large intrusion detection data

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Data mining is a process of discovering useful information from a data set. In data mining, there is a classification technique that depends on sampling accuracy to acquire a more accurate result in data classification or prediction. Therefore, a necessity in getting a good-quality sampling is required. The primary purpose of this research paper is to obtain the optimum sampling representing the original data set. Through sampling, we could minimize the total data that need to be processed. Because large amount of data requires longer processing time, reducing the amount of data with sampling will speed up the process of computing. In this study we introduced a new sampling algorithm with clustering approach applied to a network security data set. The final results showed that proposed method offer fine result for large data set sampling.

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Availability

B00533

(wh)

Available but not for loan - Missing

Detail Information

Series Title

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Call Number

533

Publisher

: Swiss German University., 2009

Collation

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Language

English

ISBN/ISSN

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Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

IT

Specific Detail Info

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Statement of Responsibility

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