图 书 馆
一种用于在噪声数据中发现相干共聚类的可扩展框架A Scalable Framework for Discovering Coherent Co-clusters in Noisy Data |
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| 课程网址: | http://videolectures.net/icml09_deodhar_sfd/ |
| 主讲教师: | Meghana Deodhar |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Clustering problems often involve datasets where only a part of the data is relevant to the problem, e.g., in microarray data anal- ysis only a subset of the genes show cohe- sive expressions within a subset of the con- ditions/features. The existence of a large number of non-informative data points and features makes it challenging to hunt for co- herent and meaningful clusters from such datasets. Additionally, since clusters could exist in different subspaces of the feature space, a co-clustering algorithm that simul- taneously clusters objects and features is of- ten more suitable as compared to one that is restricted to traditional “one-sided” clus- tering. We propose Robust Overlapping Co- Clustering (ROCC), a scalable and very ver- satile framework that addresses the problem of efficiently mining dense, arbitrarily posi- tioned, possibly overlapping co-clusters from large, noisy datasets. ROCC has several de- sirable properties that make it extremely well suited to a number of real life applications. 1 |
| 关 键 词: | 聚类问题; 数据集; 鲁棒重叠协同聚类 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：yumf |
| 阅读次数: | 70 |