图 书 馆
基于核密度估计的快速聚类Fast Clustering based on Kernel Density Estimation |
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| 课程网址: | http://videolectures.net/ida07_hinneburg_dfc/ |
| 主讲教师: | Alexander Hinneburg |
| 开课单位: | 马丁路德大学 |
| 开课时间: | 2007-10-08 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assigned to clusters by hill climbing, i.e. points going to the same local maximum are put into the same cluster. A disadvantage of Denclue 1.0 is, that the used hill climbing may make unnecessary small steps in the beginning and never converges exactly to the maximum, it just comes close. We introduce a new hill climbing procedure for Gaussian kernels, which adjusts the step size automatically at no extra costs. We prove that the procedure converges exactly towards a local maximum by reducing it to a special case of the expectation maximization algorithm. We show experimentally that the new procedure needs much less iterations and can be accelerated by sampling based methods with sacrificing only a small amount of accuracy. |
| 关 键 词: | 核密度估计; 簇由估计; 密度函数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-27：cwx |
| 阅读次数: | 108 |