| FICW： Frequent Itemset Based Text Clustering with Window Constraint |
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| 作者姓名： | ZHOU Chong LU Yansheng ZOU Lei HU Rong |
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| 作者单位： | College of Computer Science and Technology, HuazhongUniversity of Science and Technology, Wuhan 430074,Hubei, China |
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| 基金项目： | Supported by the Natural Science Foundation of Hubei Province（ ABA048） |
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| 摘 要： | Most of the existing text clustering algorithms overlook the fact that one document is a word sequence with semantic information. There is some important semantic information existed in the positions of words in the sequence. In this paper, a novel method named Frequent Itemset-based Clustering with Window （FICW） was proposed, which makes use of the semantic information for text clustering with a window constraint. The experimental results obtained from tests on three （hypertext） text sets show that FICW outperforms the method compared in both clustering accuracy and efficiency.
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| 关 键 词： | 文本聚类 搜索引擎 信息检索 语义 FICW |
| 文章编号： | 1007-1202（2006）05-1345-07 |
| 收稿时间： | 2006-03-14 |
FICW: Frequent itemset based text clustering with window constraint |
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| ZHOU Chong LU Yansheng ZOU Lei HU Rong.FICW: Frequent itemset based text clustering with window constraint[J].Wuhan University Journal of Natural Sciences,2006,11(5):1345-1351. |
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| Authors: | Zhou Chong Lu Yansheng Zou Lei Hu Rong |
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| Institution: | (1) College of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, Hubei, China |
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| Abstract: | Most of the existing text clustering algorithms overlook the fact that one document is a word sequence with semantic information. There is some important semantic information existed in the positions of words in the sequence. In this paper, a novel method named Frequent Itemset-based Clustering with Window (FICW) was proposed, which makes use of the semantic information for text clustering with a window constraint. The experimental results obtained from tests on three (hypertext) text sets show that FICW outperforms the method compared in both clustering accuracy and efficiency. |
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| Keywords: | text clustering frequent itemsets search engine |
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