stands for Digital Object Identifier
and is the unique identifier for objects on the internet. It can be used to create persistent link and to cite articles.
Using DOI as a persistent link
To create a persistent link, add「http://dx.doi.org/」
before a DOI.
For instance, if the DOI of an article is 10.5297/ser.1201.002 , you can link persistently to the article by entering the following link in your browser: http://dx.doi.org/ 10.5297/ser.1201.002 。
The DOI link will always direct you to the most updated article page no matter how the publisher changes the document's position, avoiding errors when engaging in important research.
Cite a document with DOI
When citing references, you should also cite the DOI if the article has one. If your citation guideline does not include DOIs, you may cite the DOI link.
DOIs allow accurate citations, improve academic contents connections, and allow users to gain better experience across different platforms. Currently, there are more than 70 million DOIs registered for academic contents. If you want to understand more about DOI, please visit airiti DOI Registration （ doi.airiti.com ） 。
廖又葳 , Masters Advisor：吳俊霖
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
- . V. Chandola, A. Banerjee and V. Kumar, "Anomaly detection", ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, 2009.
- . 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.
- . 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.
- . 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.
- . V. Barnett and T. Lewis, Outliers in statistical data, 3rd ed. Chichester [u.a.]: Wiley, 1994.
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