图 书 馆
集群比较的信息理论措施：是否需要修正？Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary? |
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| 课程网址: | http://videolectures.net/icml09_vinh_itm/ |
| 主讲教师: | Nguyen Xuan Vinh |
| 开课单位: | 新南威尔士大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Information theoretic based measures form a fundamental class of similarity measures for comparing clusterings, beside the class of pair-counting based and set-matching based measures. In this paper, we discuss the necessity of correction for chance for information theoretic based measures for clusterings comparison. We observe that the baseline for such measures, i.e. average value under random partitioning of a data set, does not take on a constant value, and tends to have larger variation when the ratio between the number of data points and the number of clusters is small. This effect is similar in some other non-information theoretic based measures such as the well-known Rand Index. Assuming a hypergeometric model of randomness, we derive the analytical formula for the expected mutual information value between a pair of clusterings, and then propose the adjusted version for several popular information theoretic based measures. Some examples are given to demonstrate the need and usefulness of the adjusted measures. |
| 关 键 词: | 机器学习; 聚类; 分析模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-04：lxf |
| 阅读次数: | 56 |