使用權重動態視窗之密度導向的局部離群值偵測演算法

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使用權重動態視窗之密度導向的局部離群值偵測演算法

A New Density-Based Local Outlier Detection Algorithm Using Weighted Dynamic Window

廖又葳 , Masters　　Advisor：吳俊霖　　

英文

局部離群值偵測；非監督式離群值偵測；異常值偵測；密度導向之離群值偵測；動態視窗；權重賦予；權重動態視窗； Local outlier ； Unsupervised Outlier Detection ； Anomaly Detection ； Density-Based Outlier Factor ； Dynamic window ； Weighting assignment ； Weighted dynamic window

Abstract │ Reference (16) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

An outlier is an observation sample that is distant from other observations. The outlier detection method area one of the important research topics in data analysis and pattern recognition, it has been widely used in various knowledge domains. The focus of this study is on the local outlier detection, i.e., a sample is dissimilar to its surrounding data (not global dataset). The existing density-based outlier detection algorithm - local outlier factor (LOF) has the following problems: (1) It can’t perform well when the dataset is imbalanced or their density distributions are overlapped; (2) It is sensitive to the selection of the parameter k in finding nearest neighbors. This study aims at implement a better performance density-based outlier detection method which can solve above problems. By using the dynamic window and the weighting assignment, the proposed method can detect the outlier effectively and robustly. Experiments on synthetic and real world datasets demonstrate that our proposed method yields robust and excellent performance.

Reference ( 16 ) 〈TOP〉

[2]. V. Chandola, A. Banerjee and V. Kumar, "Anomaly detection", ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, 2009.
連結：
[5]. S. Ramaswamy, R. Rastogi and K. Shim, "Efficient algorithms for mining outliers from large data sets", ACM SIGMOD Record, vol. 29, no. 2, pp. 427-438, 2000.
連結：
[8]. H. Fan, O. Zaïane, A. Foss and J. Wu, "Resolution-based outlier factor: detecting the top-n most outlying data points in engineering data", Knowledge and Information Systems, vol. 19, no. 1, pp. 31-51, 2008.
連結：
[14]. E. Schubert, A. Zimek and H. Kriegel, "Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection", Data Mining and Knowledge Discovery, vol. 28, no. 1, pp. 190-237, 2012.
連結：
[1]. V. Barnett and T. Lewis, Outliers in statistical data, 3rd ed. Chichester [u.a.]: Wiley, 1994.

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