期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 38, 期 8, 页码 9325-9333出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.01.126
关键词
Self organizing maps; Text mining; Clustering efficiency; Feature coding; Similarity computation
类别
资金
- Chinese 863 program [2007AA01Z172]
- National Natural Science Foundation of China [70773029, 60603092]
The state-of-the-art text clustering methods suffer from the huge size of documents with high-dimensional features. In this paper, we studied fast SUM clustering technology for Text Information. Our focus is on how to enhance the efficiency of text clustering system whereas high clustering qualities are also kept. To achieve this goal, we separate the system into two stages: offline and online. In order to make text clustering system more efficient, feature extraction and semantic quantization are done offline. Although neurons are represented as numerical vectors in high-dimension space, documents are represented as collections of some important keywords, which is different from many related works, thus the requirement for both time and space in the offline stage can be alleviated. Based on this scenario, fast clustering techniques for online stage are proposed including how to project documents onto output layers in SOM, fast similarity computation method and the scheme of Incremental clustering technology for real-time processing, We tested the system using different datasets, the practical performance demonstrate that our approach has been shown to be much superior in clustering efficiency whereas the clustering quality are comparable to traditional methods. (C) 2011 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据