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在最优子空间中发现非冗余K均值聚类Discovering Non‑Redundant K‑means Clusterings in Optimal Subspaces |
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| 课程网址: | http://videolectures.net/kdd2018_mautz_discovering_clusterings/ |
| 主讲教师: | Dominik Mautz |
| 开课单位: | 马克西米利安大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A huge object collection in high-dimensional space can often be clustered in more than one way, for instance, objects could be clustered by their shape or alternatively by their color. Each grouping represents a different view of the data set. The new research field of non-redundant clustering addresses this class of problems. In this paper, we follow the approach that different, non-redundant k-means-like clusterings may exist in different, arbitrarily oriented subspaces of the high-dimensional space. We assume that these subspaces (and optionally a further noise space without any cluster structure) are orthogonal to each other. This assumption enables a particularly rigorous mathematical treatment of the non-redundant clustering problem and thus a particularly efficient algorithm, which we call Nr-Kmeans (for non-redundant k-means). The superiority of our algorithm is demonstrated both theoretically, as well as in extensive experiments. |
| 关 键 词: | 巨大对象集合; 形状或颜色聚集; 非冗余k均值; 非冗余的类k均值聚类 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-01-28：cyh |
| 最后编审: | 2023-01-28：cyh |
| 阅读次数: | 27 |