Clustering Sequential Data into Hierarchical Patterns

摘要：

Sequential data, i.e. text string, is a common yet important data type. Automatically discovering patterns for sequential data is useful but challenging. In this paper, we address this task by clustering strings into hierarchical patterns. Such pattern hierarchy is particularly helpful for users to discover meaningful patterns as well as to interpret the encapsulated knowledge. We present the clustering algorithm in details and evaluate it on a large, real dataset of street addresses. The experiments demonstrate the effectiveness of our approach, making it a useful tool for analyzing and interpreting sequential data.

关键词： sequential data pattern hierarchical clustering

作者: Xinying Song Johnson Apacible

作者单位: School of Computer Science and Technology Harbin Institute of Technology Harbin, China Microsoft Research Redmond, Washington, U.S.A.

会议类型: 国际会议

会议名称: 2011 2nd International Conference on Data Storage and Data Engineering(DSDE 2011)(2011年第二届数据存储与数据工程国际会议)

会议地点: 西安

会议语种:英文

页码: 154-158

在线出版日期: 2011-05-13（万方平台首次上网日期，不代表论文的发表时间）