Clustering Text Data Streams

被引:1
作者
Yu-Bao Liu
Jia-Rong Cai
Jian Yin
Ada Wai-Chee Fu
机构
[1] Sun Yat-Sen University,Department of Computer Science
[2] the Chinese University of Hong Kong,Department of Computer Science and Engineering
来源
Journal of Computer Science and Technology | 2008年 / 23卷
关键词
clustering; database applications; data mining; text data streams;
D O I
暂无
中图分类号
学科分类号
摘要
Clustering text data streams is an important issue in data mining community and has a number of applications such as news group filtering, text crawling, document organization and topic detection and tracing etc. However, most methods are similarity-based approaches and only use the TF*IDF scheme to represent the semantics of text data and often lead to poor clustering quality. Recently, researchers argue that semantic smoothing model is more efficient than the existing TF*IDF scheme for improving text clustering quality. However, the existing semantic smoothing model is not suitable for dynamic text data context. In this paper, we extend the semantic smoothing model into text data streams context firstly. Based on the extended model, we then present two online clustering algorithms OCTS and OCTSM for the clustering of massive text data streams. In both algorithms, we also present a new cluster statistics structure named cluster profile which can capture the semantics of text data streams dynamically and at the same time speed up the clustering process. Some efficient implementations for our algorithms are also given. Finally, we present a series of experimental results illustrating the effectiveness of our technique.
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收藏
页码:112 / 128
页数:16
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