[1]应玉龙.一种基于特征属性的Web用户模糊聚类改进算法[J].计算机技术与发展,2012,(07):95-98.
　YING Yu-long.An Improved Web Users Fuzzy Clustering Algorithm Based on Features Property[J].,2012,(07):95-98.
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一种基于特征属性的Web用户模糊聚类改进算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2012年07期
页码:
95-98
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
An Improved Web Users Fuzzy Clustering Algorithm Based on Features Property
文章编号:
1673-629X（2012）07-0095-04
作者:
应玉龙
浙江纺织服装学院信息工程分院
Author(s):
YING Yu-long
Information Engineering School, Zhejiang Textile ＆ Fashion College
关键词:
特征属性; Web用户; 模糊聚类; 模糊C均值算法
Keywords:
features property ; Web users;  fuzzy clustering;  FCM
分类号:
TP391
文献标志码:
A
摘要:
为降低传统FCM算法的计算复杂性，提高Web用户聚类的效果，文中提出了一种改进的基于特征属性的Web用户模糊聚类算法。首先通过用户访问页面的次数和时间建立Web用户兴趣度矩阵，并根据商品的特征属性值将Web用户兴趣度矩阵映射为用户对特征属性的偏好矩阵，从而有效降低数据稀疏性；然后以此为数据集，对传统的FCM算法进行了改进，将聚类中心分为活动和稳定两种，忽略稳定聚类中的距离计算以降低计算复杂性。最后通过仿真实验证实了新算法的有效性和可行性
Abstract:
In this paper, present an improved Web users fuzzy clustering algorithm based on features property to reduce the computational complexity of conventional fuzzy c-means clusering algorithm and improve the effect of Web users clusering. First,establish Web user＇s interest degree matrix through the times and time of user＇s visited pages, mapping the Web user＇s interest degree matrix into the user＇s features property preference matrix according to the features property of item to reduce the data sparseness effectively. Based on the features property preference matrix, improved the conventional fuzzy c-means clusering algorithm. The proposed method first classifies cluster centers into active and stable groups,then skips the distance calculations for stable clusters in the iterative process to reduce the computational complexity of conventional fuzzy c-means clustering algorithm. Finally ,the simulation demonstrates the feasibility and validity of the proposed method

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备注/Memo

备注/Memo:
宁波市自然科学基金（2010A610118）;宁波市先进纺织技术与服装CAD重点实验室（2011ZDSYS-A-004）应玉龙（1979-），男，浙江浦江人，硕士研究生，研究方向为数字挖掘、信息推荐、图像检索
更新日期/Last Update: 1900-01-01