图 书 馆
提高组合的MRFs聚类稳定性Improving Clustering Stability with Combinatorial MRFs |
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| 课程网址: | http://videolectures.net/kdd09_bekkerman_icswc/ |
| 主讲教师: | Ron Bekkerman |
| 开课单位: | 海法大学 |
| 开课时间: | 2009-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | As clustering methods are often sensitive to parameter tuning, obtaining stability in clustering results is an important task. In this work, we aim at improving clustering stability by attempting to diminish the influence of algorithmic inconsistencies and enhance the signal that comes from the data. We propose a mechanism that takes m clusterings as input and outputs $m$ clusterings of comparable quality, which are in higher agreement with each other. We call our method the Clustering Agreement Process (CAP). To preserve the clustering quality, CAP uses the same optimization procedure as used in clustering. In particular, we study the stability problem of randomized clustering methods (which usually produce different results at each run). We focus on methods that are based on inference in a combinatorial Markov Random Field (or Comraf, for short) of a simple topology. We instantiate CAP as inference within a more complex, bipartite Comraf. We test the resulting system on four datasets, three of which are medium-sized text collections, while the fourth is a large-scale user/movie dataset. First, in all the four cases, our system significantly improves the clustering stability measured in terms of the macro-averaged Jaccard index. Second, in all the four cases our system managed to significantly improve clustering quality as well, achieving the state-of-the-art results. Third, our system significantly improves stability of consensus clustering built on top of the randomized clustering solutions. |
| 关 键 词: | 聚类协议的过程; 随机聚类方法; 共识聚类 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-31：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 61 |