图 书 馆
使用压缩采样后速度近似文本文档聚类Fast approximate text document clustering using Compressive Sampling |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_park_fast/ |
| 主讲教师: | Laurence A. F. Park |
| 开课单位: | 墨尔本大学 |
| 开课时间: | 2011-10-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Document clustering involves repetitive scanning of a document set, therefore as the size of the set increases, the time required for the clustering task increases and may even become impossible due to computational constraints. Compressive sampling is a feature sampling technique that allows us to perfectly reconstruct a vector from a small number of samples, provided that the vector is sparse in some known domain. In this article, we apply the theory behind compressive sampling to the document clustering problem using k-means clustering. We provide a method of computing high accuracy clusters in a fraction of the time it would have taken by directly clustering the documents. This is performed by using the Discrete Fourier Transform and the Discrete Cosine Transform. We provide empirical results showing that compressive sampling provides a 14 times increase in speed with little reduction in accuracy on 7,095 documents, and we also provide a very accurate clustering of a 231,219 document set, providing 20 times increase in speed when compared to performing k-means clustering on the document set. This shows that compressive clustering is a very useful tool that can be used to quickly compute approximate clusters. |
| 关 键 词: | 文档聚类; 计算约束; 压缩采样 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-03：lxf |
| 阅读次数: | 25 |