Weighted k-Means Algorithm Based Text Clustering

被引：4
|
作者：
Chen, Xiuguo [1 ]
Yin, Wensheng [1 ]
Tu, Pinghui [2 ]
Zhang, Hengxi [2 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan 430074, Peoples R China
[2] Wuhan Commun Basic Construct Project Qual Supervi, Wuhan 430015, Peoples R China
来源：
IEEC 2009: FIRST INTERNATIONAL SYMPOSIUM ON INFORMATION ENGINEERING AND ELECTRONIC COMMERCE, PROCEEDINGS | 2009年
关键词：
text clustering; k-means clustering; weighting; text mining;
D O I：
10.1109/IEEC.2009.17
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
this paper proposes a weighted k-means clustering algorithm based on k-means (MacQueen, 1967; Anderberg, 1973) algorithm, and it can be used to cluster texts. Firstly, the weighted k-means algorithm changes the descriptive approach of text objects, and converts the categorical attributes to numeric ones to measure the dissimilarity of text objects by Euclidean distance; then, the weighted k-means algorithm uses weight vector to decrease the affects of irrelevant attributes and reflect the semantic information of text objects. Through an experiment, the weighted k-means algorithm is demonstrated to be more effective than k-means algorithm when used to cluster texts.
引用
收藏
页码：51 / +
页数：2
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