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图簇是否有最佳质量?Is there a best quality metric for graph clusters? |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_almeida_best/ |
| 主讲教师: | Hélio Almeida |
| 开课单位: | 米纳斯吉拉斯联邦大学 |
| 开课时间: | 2011-10-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Graph clustering, the process of discovering groups of similar vertices in a graph, is a very interesting area of study, with applications in many different scenarios. One of the most important aspects of graph clustering is the evaluation of cluster quality, which is important not only to measure the effectiveness of clustering algorithms, but also to give insights on the dynamics of relationships in a given network. Many quality evaluation metrics for graph clustering have been proposed in the literature, but there is no consensus on how do they compare to each other and how well they perform on different kinds of graphs. In this work we study five major graph clustering quality metrics in terms of their formal biases and their behavior when applied to clusters found by four implementations of classic graph clustering algorithms on five large, real world graphs. Our results show that those popular quality metrics have strong biases toward incorrectly awarding good scores to some kinds of clusters, especially seen in larger networks. They also indicate that currently used clustering algorithms and quality metrics do not behave as expected when cluster structures are different from the more traditional, clique-like ones. |
| 关 键 词: | 图形聚类; 测量聚类算法; 评估指标 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-17：cwx |
| 阅读次数: | 34 |