报告人：孙浩军教授 汕头大学
报告题目：聚类交叠分析和子空间聚类
时 间：2016年3月31日 (星期四)上午 09:00
地 点：仓山校区成功楼603报告厅
主 办：数学与计算机科学学院
参加对象：计算机系教师、研究生
报告摘要：The ability of a clustering algorithm to deal with overlapping clusters is a major indicator of its efficiency. However, the phenomenon of cluster overlapping is still not mathematically well characterized, especially in multivariate cases. We introduce the novel concept of the ridge curve and establish a theory on the degree of overlap between two components. Based on this theory, we develop an algorithm for calculating the overlap rate. As an example, we use this algorithm to calculate the overlap rates between the classes in some data sets.
Clustering high-dimensional data is a challenging task in data mining, and clustering high-dimensional categorical data is even more challenging because it is more difficult to measure the similarity between categorical objects. We propose a hierarchical algorithm with attribute weighting for clustering high-dimensional categorical data, based on a recently proposed information-theoretical concept named holo-entropy. The algorithm proposes new ways of exploring entropy, holo-entropy and attribute weighting in order to determine the feature subspace of a cluster and to merge clusters even though their feature subspaces differ.
专家简介：孙浩军，博士，教授， 1982年毕业于河北大学数学系， 1982-2000年在河北大学任教，2000-2005年在加拿大舍布克大学留学，2005年获博士学位。同年回国工作，现任汕头大学工学院计算机系教授。其研究涉及了多个科学领域，其中包括模式识别、数据挖掘、图像处理和理解、信息系统、神经网络、信号处理等。近年来主持国家自然基金、省基金以及横向课题的研究，在国际国内期刊、国际会议发表论文多篇。
