Temporal Skeletonization on Sequential Data: Patterns, Categorization, and Visualization¶
Hui Xiong
Associate Professor
TULIP Lab, Deakin University, Australia
Abstract: Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in a sequence. In this talk, we introduce a temporal skeletonization approach to proactively reduce the representation of
sequences, so as to expose their hidden multi-level temporal structures. The key idea is to summarize the temporal correlations in an undirected graph. Then, the "skeleton" of the graph serves as a higher granularity on which hidden temporal patterns are more likely to be identified. Finally, experimental results on real-world data have shown that the proposed approach can greatly alleviate the problem of curse of cardinality for the challenging tasks of sequential pattern mining and clustering. Also, the evaluation on a Business-to-Business (B2B) marketing application demonstrates that our approach can effectively discover critical buying paths frOm noisy marketing data.
Bio: Dr．Hui Xiong is currently an Associate Professor and the Vice Chair of the Management Science and Information Systems Department, and the Director of Rutgers Center for Information Assu rance at Rutgers, the State University of New Jersey. Dr．Xiong received his Ph.D. in Computer Science from the University of Minnesota (UMN), USA, in 2005, the B.E. degree in Automation from the University of Science and Technology of China (USTC), China, and the M.S. degree in Computer Science from the National University of Singapore (NUS), Singapore. His general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications.