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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/125518


    Title: 使用正規隨機漫步及相似度進行異常偵測
    Anomaly Detection Using Regulated Random Walk and Similarity Degree
    Authors: 陳柏龍
    Chen, Po-Lung
    Contributors: 周珮婷
    Chou, Pei-Ting
    陳柏龍
    Chen, Po-Lung
    Keywords: 異常偵測
    相似度
    正規隨機漫步
    多尺度
    自我調整
    Anomaly detection
    Similarity
    Regulated random walk
    Multi-scale
    Self-tuning
    Date: 2019
    Issue Date: 2019-09-05 15:42:18 (UTC+8)
    Abstract: 資料雲幾何樹是一個透過正規隨機漫步捕捉資料結構，再進行分群的一個演算法。本論文從資料雲幾何樹的概念中延伸出了兩種異常偵測的方法，第一種是使用樣本間的相似度加總來進行異常偵測，第二種則是透過正規隨機漫步探索數據，以探索到的時間點做為異常值。而在使用多尺度的模擬資料時，發現演算法表現不穩定，因此使用了self-tuning的策略來改良演算法，能克服在資料多尺度時進行異常偵測的問題，最後在實際資料上和經典方法LOF比較。
    Data cloud geometry tree is a clustering algorithm that explores data structures by regulated random walk. Based on the concept of data cloud geometry tree, the current study proposes two anomaly detection methods. The first method uses sum of similarities between samples for anomaly detection. The second method explores data through regulated random walk to detect unusual pattern. Samples that were later explored are treated as abnormal. However, the performance of the proposed algorithms are unstable when dealing with multi-scaled simulated data. Therefore, self-tuning strategy is applied to improve the performance of algorithms and to overcome the anomaly detection problem for multi-scaled data. Finally, the performance of proposed methods are compared to the performance resulting from the classical method, LOF, with many real examples.
    Reference: Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: identifying density-based local outliers. Paper presented at the ACM sigmod record.
    Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 15.
    Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Paper presented at the Kdd.
    Fushing, H., & McAssey, M. P. (2010). Time, temperature, and data cloud geometry. Phys Rev E Stat Nonlin Soft Matter Phys, 82(6 Pt 1), 061110. doi:10.1103/PhysRevE.82.061110
    Goldstein, M. (2012). FastLOF: An expectation-maximization based local outlier detection algorithm. Paper presented at the Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).
    Grubbs, F. E. (1950). Sample criteria for testing outlying observations. The Annals of Mathematical Statistics, 21(1), 27-58.
    He, Z., Xu, X., & Deng, S. (2003). Discovering cluster-based local outliers. Pattern Recognition Letters, 24(9-10), 1641-1650.
    Kriegel, H.-P., & Zimek, A. (2008). Angle-based outlier detection in high-dimensional data. Paper presented at the Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining.
    Lazarevic, A., & Kumar, V. (2005). Feature bagging for outlier detection. Paper presented at the Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining.
    Lee, Y.-J., Yeh, Y.-R., & Wang, Y.-C. F. (2012). Anomaly detection via online oversampling principal component analysis. IEEE Transactions on Knowledge and Data Engineering, 25(7), 1460-1470.
    Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation forest. Paper presented at the 2008 Eighth IEEE International Conference on Data Mining.
    Maaten, L. v. d., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(Nov), 2579-2605.
    Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. Paper presented at the Advances in neural information processing systems.
    Pokrajac, D., Lazarevic, A., & Latecki, L. J. (2007). Incremental local outlier detection for data streams. Paper presented at the 2007 IEEE symposium on computational intelligence and data mining.
    Sakurada, M., & Yairi, T. (2014). Anomaly detection using autoencoders with nonlinear dimensionality reduction. Paper presented at the Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis.
    Zelnik-Manor, L., & Perona, P. (2005). Self-tuning spectral clustering. Paper presented at the Advances in neural information processing systems.
    Zenati, H., Foo, C. S., Lecouat, B., Manek, G., & Chandrasekhar, V. R. (2018). Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222.
    Description: 碩士
    國立政治大學
    統計學系
    106354026
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106354026
    Data Type: thesis
    DOI: 10.6814/NCCU201900895
    Appears in Collections:[Department of Statistics] Theses

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