图 书 馆
在概念漂移高维轨迹的在线聚类Online Clustering of High-Dimensional Trajectories under Concept Drift |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_krempl_trajectories/ |
| 主讲教师: | Georg Krempl |
| 开课单位: | 布拉格捷克技术大学 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Historical transaction data are collected in many applications, e.g., patient histories recorded by physicians and customer transactions collected by companies. An important question is the learning of models upon the primary objects (patients, customers) rather than the transactions, especially when these models are subjected to drift. We address this problem by combining advances of online clustering on multivariate data with the trajectory mining paradigm. We model the measurements of each individual primary object (e.g. its transactions), taken at irregular time intervals, as a trajectory in a high-dimensional feature space. Then, we cluster individuals with similar trajectories to identify sub-populations that evolve similarly, e.g. groups of customers that evolve similarly or groups of employees that have similar careers. We assume that the multivariate trajectories are generated by drifting Gaussian Mixture Models. We study (i) an EM-based approach that clusters these trajectories incrementally as a reference method that has access to all the data for learning, and propose (ii) an online algorithm based on a Kalman filter that efficiently tracks the trajectories of Gaussian clusters. We show that while both methods approximate the reference well, the algorithm based on a Kalman filter is faster by one order of magnitude compared to the EM-based approach. |
| 关 键 词: | 计算机科学; 数据挖掘; 时态和流采矿 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-06：魏雪琼(课程编辑志愿者) |
| 阅读次数: | 56 |