图 书 馆
可可：编码参数的孤立点检测的成本CoCo: Coding Cost for Parameter-free Outlier Detection |
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| 课程网址: | http://videolectures.net/kdd09_muller_cococcpfod/ |
| 主讲教师: | Nikola Müller |
| 开课单位: | 马克斯普朗克研究所 |
| 开课时间: | 2009-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | How can we automatically spot all outstanding observations in a data set? This question arises in a large variety of applications, e.g. in economy, biology and medicine. Existing approaches to outlier detection suffer from one or more of the following drawbacks: The results of many methods strongly depend on suitable parameter settings being very difficult to estimate without background knowledge on the data, e.g. the minimum cluster size or the number of desired outliers. Many methods implicitly assume Gaussian or uniformly distributed data, and/or their result is difficult to interpret. To cope with these problems, we propose CoCo, a technique for parameter-free outlier detection. The basic idea of our technique relates outlier detection to data compression: Outliers are objects which can not be effectively compressed given the data set. To avoid the assumption of a certain data distribution, CoCo relies on a very general data model combining the Exponential Power Distribution with Independent Components. We define an intuitive outlier factor based on the principle of the Minimum Description Length together with an novel algorithm for outlier detection. An extensive experimental evaluation on synthetic and real world data demonstrates the benefits of our technique. **//Disclaimer:// VideoLectures.Net emphasizes that the length of this video is not standard and was cut because of poor sound quality conditions provided in the lecture auditorium.** |
| 关 键 词: | 数据集中; 参数设置; 孤立点检测数据压缩 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-12-24：liyy |
| 阅读次数: | 61 |