Key Issues and Theoretical Framework on Moving Objects Data Mining

摘要：

Considering technical difficulties and bottlenecks in moving objects data mining, such as massive movement data, high dimensional data, topological complexity, and knowledge semantic representation etc., this paper focuses on the study of theory and methods of moving objects data mining. First, it presents two key scientific issues of the research topic, i.e. integration and modeling of heterogeneous data, and information aggregation and interpretation. Second, a theoretical framework of moving object data mining is proposed based on different perspectives of spacetime data→space-time concept→space-time pattern. Two aspects of the framework are then discussed in details, including (1) moving objects data modeling and semantic expression;(2) mining methods and algorithms of association rules based on concept lattice. Finally, future works are discussed.

关键词： Moving objects Spatio-temporal data model Spatio-temporal data mining Ontology Concept lattice Natural language

作者: Rong Xie Xin Luo

作者单位: International School of Software Wuhan University,430079 Wuhan Hubei State Key Lab of Digital Manufacturing Equipment and Technology,Huazhong University of Science and T

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 577-584

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）