Frequent Pattern Trend Analysis in Social Networks
This paper describes an approach to identifying and comparing frequent pattern trends in social networks. A frequent pattern trend is defined as a sequence of time-stamped occurrence (support) values for specific frequent patterns that exist in the data. The trends are generated according to epochs. Therefore, trend changes across a sequence epochs can be identified. In many cases, a great many trends are identified and difficult to interpret the result. With a combination of constraints, placed on the frequent patterns, and clustering and cluster analysis techniques, it is argued that analysis of the result is enhanced. Clustering technique uses a Self Organising Map approach to produce a sequence of maps, one per epoch. These maps can then be compared and the movement of trends identified. This Frequent Pattern Trend Mining framework has been evaluated using two non-standard types of social networks, the cattle movement network and the insurance quote network.
Social Networks Pattern Mining Trend Mining Trend Analysis
Puteri N.E. Nohuddin Rob Christley Frans Coenen Yogesh Patel Christian Setzkorn Shane Williams
Department of Computer Science University of Liverpool UK School of Veterinary Science University of Liverpool and National Centre for Zoonosis Research Leahu Deeside Insurance Ltd. Deeside UK Department of Computer Science University of Liverpool UK Deeside Insurance Ltd. Deeside UK
国际会议
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)
重庆
英文
358-369
2010-11-19（万方平台首次上网日期，不代表论文的发表时间）