图 书 馆
短消息到通用域的高效聚类Efficient Clustering of Short Messages into General Domains |
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| 课程网址: | https://videolectures.net/videos/icwsm2013_tsur_general_domains |
| 主讲教师: | Oren Tsur |
| 开课单位: | 信息不详。欢迎您在右侧留言补充。 |
| 开课时间: | 2014-04-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The ever increasing activity in social networks is mainly manifested by a growing stream of status updating or microblogging. The massive stream of updates emphasizes the need for accurate and efficient clustering of short messages on a large scale. Applying traditional clustering techniques is both inaccurate and inefficient due to sparseness. This paper presents an accurate and efficient algorithm for clustering Twitter tweets. We break the clustering task into two distinctive tasks/stages: (1) batch clustering of user annotated data, and (2) online clustering of a stream of tweets. In the first stage we rely on the habit of `tagging', common in social media streams (e.g. hashtags), thus the algorithm can bootstrap on the tags for clustering of a large pool of hashtagged tweets. The stable clusters achieved in the first stage lend themselves for online clustering of a stream of (mostly) tagless messages. We evaluate our results against gold-standard classification and validate the results by employing multiple clusteringevaluation measures (information theoretic, paired, F and greedy). We compare our algorithm to a number of otherclustering algorithms and various types of feature sets. Results show that the algorithm presented is both accurate andefficient and can be easily used for large scale clustering of sparse messages as the heavy lifting is achieved ona sublinear number of documents. |
| 关 键 词: | 推特推文聚类算法; 社交网络; 亚线性数量 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-05-08：yuhongrui |
| 最后编审: | 2025-05-08：yuhongrui |
| 阅读次数: | 4 |