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- W1974143005 abstract "The increase in Internet and Internet based application, the business premises have now spread throughout the world. Due to the extreme competitions among the business, one tries to demolish other. Hence, secure product design techniques should be adopted. To protect the applications from intruder, intrusion detection system becomes utmost requirement for every organization. In intrusion detection models enormous quantity of training data is required. As a result, sophisticated algorithms and high computational resources are required. In Intrusion Detection System, to separate normal activities from abnormal activities clustering algorithms are used. To select an efficient clustering algorithm is a challenging task. In this paper, a comparison has been made between K-Means and C-Means clustering on intrusion datasets. The simulation contains all proximity measures of K-Means and C-Means clustering techniques. The accuracy of these clustering algorithms is compared using the confusion matrix. The result shows that K-Means provides better clustering accuracy in comparison with C-Means. Therefore, to design intelligent intrusion detection product K-Means is a better option." @default.
- W1974143005 created "2016-06-24" @default.
- W1974143005 creator A5010597928 @default.
- W1974143005 date "2014-01-01" @default.
- W1974143005 modified "2023-09-25" @default.
- W1974143005 title "A Study of K-Means and C-Means Clustering Algorithms for Intrusion Detection Product Development" @default.
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- W1974143005 doi "https://doi.org/10.7763/ijimt.2014.v5.515" @default.
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