.png){kind=icon}
返回
Text clustering with feature selection by using statistical data
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2007.190740.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Feature, selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus. In this paper, we propose a new supervised feature selection method, named CHIR, which is based on the chi(2) statistic and new statistical data that can measure the positive term-category dependency. We also propose a new text clustering algorithm, named Text Clustering with Feature Selection (TCFS). TCFS can incorporate CHIR to identify relevant features (i.e., terms) iteratively, and the clustering becomes 6 learning process. We compared TCFS and the K-means clustering algorithm in combination with different feature selection methods for various real data sets. Our experimental results show that TCFS with CHIR has better clustering accuracy in terms of the F-measure and the purity.
Keyword:
text clustering
text mining
chi(2) Statistic
feature selection
performance analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.9K
被引数:
3.2W
机构
引用论文
.png){kind=icon}
Cyclic 3′,5′-nucleotide phosphodiesterase effect of binding protein on the hydrolysis of cyclic AMP
PREAI
分享收藏分享收藏
Effects of circumscribed cortical lesions upon somesthetic and visual discrimination in the monkey.
PREAI
分享收藏A Study of Effects of Design Parameters on Transient Response and Injection Rate Shaping for a Common Rail Injector System设计参数对共轨喷油器系统瞬态响应和喷油率整形影响的研究
PREAI
分享收藏
查看更多内容