DETEKSI DATA PENCILAN MENGGUNAKAN K_MEANS CLUSTERING
Keywords:
clustering, data pencilan, k_meansAbstract
Deteksi data pencilan sangat penting dan mempunyai banyak aplikasi diantaranya adalah identifikasi adanya pengacauan dan sumbatan dalam jaringan komputer, aktivitas kriminal dalam e-commerce, deteksi pemalsuan kartu kredit dan aktivitas-aktivitas yang mencurigakan. Dalam tulisan ini dibicarakan deteksi data pencilan menggunakan metode clustering k_means, dengan jumlah cluster dianggap parameter dan secara incremental ditambah sampai didapat cluster kecil yang kemudian dianggap sebagai data pencilan. Akhirnya diberikan ilustrasi bagaimana metode tersebut diterapkan pada beberapa kelompok data.
Downloads
Download data is not yet available.
References
Breunig, M., H. Kriegel, R. Ng and J. Sander, 2000, Lof: identifying density-based local outliers. In Proceedings of 2000 ACM SIGMOD International Conference on Management of Data, ACM Press, 93-104
Han, J. and M. Chamber , 2006. Data Mining: Concepts and Techniques, Morgan Kaufmann, 2nd ed.
Hawkins, D., 1980, Identifications of Outliers, Chapman and Hall, London
Hodge, V. and J. Austin, 2004. A Survey of Outlier Detection Methodologies, Artificial Intelligence Review, 22: 85–126.
Johnson,R.A,Wichren,D,2004.Applied Multivariate Analysis. Prentice Hall
Knorr, E., R. Ng, and V. Tucakov, 2000, Distance-based Outliers: Algorithms and Applications, VLDB Journal, 8(3-4): 237-253
Loureiro,A., L. Torgo and C. Soares, 2004. Outlier Detection using Clustering Methods: a DataCleaning Application, in Proceedings of KDNet Symposium on Knowledge-based Systems for the Public Sector. Bonn, Germany.
Niu, K., C. Huang, S. Zhang, and J. Chen, 2007. ODCC: Outlier Detection using Distance Distribution Clustering, T. Washio et al. (Eds.) : PAKDD 2007 Workshops, Lecture Notes in Artificial Intelligence (LNAI) 4819, pp. 332-343, Springer-Verlag.
Zhang, J. and H. Wang, 2007. Detecting outlying subspaces for high-dimensional data: the new Task, Algorithms, and Performance, Knowledge and Information Systems, 10(3): 333-355.
Han, J. and M. Chamber , 2006. Data Mining: Concepts and Techniques, Morgan Kaufmann, 2nd ed.
Hawkins, D., 1980, Identifications of Outliers, Chapman and Hall, London
Hodge, V. and J. Austin, 2004. A Survey of Outlier Detection Methodologies, Artificial Intelligence Review, 22: 85–126.
Johnson,R.A,Wichren,D,2004.Applied Multivariate Analysis. Prentice Hall
Knorr, E., R. Ng, and V. Tucakov, 2000, Distance-based Outliers: Algorithms and Applications, VLDB Journal, 8(3-4): 237-253
Loureiro,A., L. Torgo and C. Soares, 2004. Outlier Detection using Clustering Methods: a DataCleaning Application, in Proceedings of KDNet Symposium on Knowledge-based Systems for the Public Sector. Bonn, Germany.
Niu, K., C. Huang, S. Zhang, and J. Chen, 2007. ODCC: Outlier Detection using Distance Distribution Clustering, T. Washio et al. (Eds.) : PAKDD 2007 Workshops, Lecture Notes in Artificial Intelligence (LNAI) 4819, pp. 332-343, Springer-Verlag.
Zhang, J. and H. Wang, 2007. Detecting outlying subspaces for high-dimensional data: the new Task, Algorithms, and Performance, Knowledge and Information Systems, 10(3): 333-355.
Downloads
Published
2010-06-30
How to Cite
Widyastuti, N. (2010). DETEKSI DATA PENCILAN MENGGUNAKAN K_MEANS CLUSTERING. Jurnal Teknologi, 3(1), 16–21. Retrieved from https://ejournal.akprind.ac.id/index.php/jurtek/article/view/837
Issue
Section
Articles
