图 书 馆
boostcluster：升压的成对约束聚类BoostCluster: Boosting Clustering by Pairwise Constraints |
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| 课程网址: | http://videolectures.net/kdd07_liu_bc/ |
| 主讲教师: | Yi Liu |
| 开课单位: | 南达科他州立大学 |
| 开课时间: | 2007-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints. However, these studies focus on designing special clustering algorithms that can effectively exploit the pairwise constraints. We present a boosting framework for data clustering, termed as BoostCluster, that is able to iteratively improve the accuracy of any given clustering algorithm by exploiting the pairwise constraints. The key challenge in designing a boosting framework for data clustering is how to influence an arbitrary clustering algorithm with the side information since clustering algorithms by definition are unsupervised. The proposed framework addresses this problem by dynamically generating new data representations at each iteration that are, on the one hand, adapted to the clustering results at previous iterations by the given algorithm, and on the other hand consistent with the given side information. Our empirical study shows that the proposed boosting framework is effective in improving the performance of a number of popular clustering algorithms (Kmeans, partitional SingleLink, spectral clustering), and its performance is comparable to the state-of-the-art algorithms for data clustering with side information. |
| 关 键 词: | 数据聚类; 聚类算法; 数据信息 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 48 |