硕士生导师
个人信息Personal Information
学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
报考该导师研究生的方式
欢迎你报考王红军老师的研究生，报考有以下方式：
1、参加西南交通大学暑期夏令营活动，提交导师意向时，选择王红军老师，你的所有申请信息将发送给王红军老师，老师看到后将和你取得联系，点击此处参加夏令营活动
2、如果你能获得所在学校的推免生资格，欢迎通过推免方式申请王红军老师研究生，可以通过系统的推免生预报名系统提交申请，并选择意向导师为王红军老师，老师看到信息后将和你取得联系，点击此处推免生预报名
3、参加全国硕士研究生统一招生考试报考王红军老师招收的专业和方向，进入复试后提交导师意向时选择王红军老师。
4、如果你有兴趣攻读王红军老师博士研究生，可以通过申请考核或者统一招考等方式报考该导师博士研究生。
Hyper-ellipsoidal clustering technique for evolving data stream
影响因子：8.139
DOI码：10.1016/j.knosys.2013.11.022
所属单位：西南交通大学
发表刊物：KNOWLEDGE-BASED SYSTEMS
刊物所在地：NETHERLANDS
关键字：Data mining Decision support systems Hyper-ellipsoidal clustering Evolving data stream Data clustering
摘要：Data mining has become a key ingredient in establishing intelligent decision support systems. As one of main branches in data mining, data stream clustering has received much attention over the past decade. Most existing data stream clustering techniques count on Euclidean distance metric for finding similar objects and hence produce spherical clusters which are not always suitable to represent the data. Moreover, in most of the real world problems, we come across the data of varying density which cannot be handled by density-based clustering techniques. In this paper, we introduce a new clustering technique called Hyper-Ellipsoidal Clustering for Evolving data Stream (HECES) based on the recently proposed HyCARCE algorithm. In HECES, a few modifications in the HyCARCE algorithm are made for handling stream clustering problem: sliding window model is used to handle incoming stream of data to minimize the impact of the obsolete information on recent clustering results; shrinkage technique is used to avoid the singularity issue in finding the covariance of correlated data; a novel technique for merging the initial ellipsoids is used to obtain the final clusters instead of a computationally intensive process of expansion and adjustment. HECES relies on Mahalanobis distance metric to cluster the data points and hence results in ellipsoidal shaped clusters. It can successfully handle data of varying density. Experiments on various synthetic and real datasets for clustering streaming data provide a comparative validation of our approach.
合写作者：Yan Yang, Hongjun Wang
第一作者：Muhammad Zia-urRehman
论文类型：学术论文
通讯作者：李天瑞
论文编号：20143600059596
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume 70
期号：November 2014
页面范围：Pages 3-14
ISSN号：0950-7051
是否译文：否
发表时间：2013-11-08