Abstract
To solve the shortages of traditional k-means algorithm that it needs to input the clustering number and it is sensitive to initial clustering center, the improved k-means algorithm is put forward. In the improved algorithm, each data object will be represented by the number of points around it in a certain region. Data objects will be clustered on the basis of that the distances between data objects belonging to different kinds are farther than the ones between the same. Both k-means and improved k-means are used in intrusion detection, which shows that the improved can overcome inherent disadvantages of k-means and has good clustering results.
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Wei, M., Xia, L., Su, J. (2011). Research on the Application of Improved K-Means in Intrusion Detection. In: Liu, C., Chang, J., Yang, A. (eds) Information Computing and Applications. ICICA 2011. Communications in Computer and Information Science, vol 243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27503-6_92
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DOI: https://doi.org/10.1007/978-3-642-27503-6_92
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