Date of Publication :7th September 2017
Abstract: Outlier detection has been used to detect the outlier and, where appropriate, eliminate outliers from various types of data. It has vital applications in the field of fraud detection, network robustness analysis, Insider Trading Detection, email spam detection, Medical and Public Health Outlier Detection, Industrial Damage Detection, Image processing fraud detection, marketing, network sensors and intrusion detection. In this paper, we propose a kmean clustering and neural network as novel to detect the outlier in network analysis. Especially in a social network, k means clustering and neural network is used to find the community overlapped user in the network as well as it finds more kclique which describe the strong coupling of data. In this paper, we propose that this method is efficient to find out outlier in social network analyses. Moreover, we show the effectiveness of this new method using the experiments data. (Abstract)
Reference :
-
- Lekhi, N., & Mahajan, M. (2015). Outlier Reduction using Hybrid Approach in Data Mining. International Journal of Modern Education and Computer Science, 7(5), 43.
- Pamula, R., Deka, J. K., & Nandi, S. (2011, February). An outlier detection method based on clustering. In Emerging Applications of Information Technology (EAIT), 2011 Second International Conference on (pp. 253-256). IEEE.
- Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May). LOF: identifying densitybased local outliers. In ACM sigmod record (Vol. 29, No. 2, pp. 93-104). ACM.
- E. Levent, M. Steinbach and V. Kumar, "A New Shared Nearest Neighbor Clustering Algorithm and its Applications"
- A.arning,R.Agrawal and P.Raghavan, A linear method for deviation detection in large database,1996.
- Kaur, P., & Kaur, P. AN OVERVIEW OF DATA MINING TOOLS,2015
- Marghny, M. H., and Ahmed I. Taloba. "Outlier Detection using Improved Genetic K-means." Available at SSRN 2545143 (2011).
- Shashikala, H. M., George, R., & Shujaee, K. A. (2015, April). Outlier detection in network data using the Betweenness Centrality. In SoutheastCon 2015 (pp. 1-5). IEEE.
- Kumar, V., Kumar, S., & Singh, A. K. Outlier Detection: A ClusteringBased Approach. International Journal of Science and Modern Engineering (IJISME), ISSN, 2319-6386.
- Palla, Gergely, Imre Derényi, Illés Farkas, and Tamás Vicsek. "Uncovering the overlapping community structure of complex networks in nature and society." Nature 435, no. 7043 (2005): 814-818.
- Yildiz, Hakan, and Christopher Kruegel. "Detecting social cliques for automated privacy control in online social networks." In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012 IEEE International Conference on, pp. 353-359. IEEE, 2012.