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@article{144943,
        author = {Tejhaskar A and Vigneshwar R},
        title = {Outlier analysis in complex network using DBSCAN and Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {6},
        pages = {328-333},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144943},
        abstract = {Outlier detection has been used to detect the outlier and, where appropriate, eliminate outliers from various types ofdata. 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 DBSCAN clustering and neural network as novel to detect the outlier in network analysis. Especially in a social network, DBSCAN 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. },
        keywords = {Outlier Detection; Network Data; Adjacency Matrix; DBSCAN Clustering; Neural Network. },
        month = {},
        }