Sequential pattern analysis with right granularity

Chuanren Liu, Kai Zhang, Hui Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in a sequence. This is a fundamental problem in data mining with diversified applications in many science and business fields, such as multimedia analysis (motion gesture/video sequence recognition), marketing analytics (buying path prediction), and financial modelling (trend of stock prices). Given the overwhelming scale and the heterogeneous nature of the sequential data, new techniques for sequential pattern analysis are required to derive competitive advantages and unlock the power of the big data. In this dissertation, we develop novel approaches for sequential pattern analysis with applications in dynamic business environments, including operation and management tasks in healthcare industry as well as B2B (Business-to-Business) marketing. Our major contribution is to identify the right granularity for sequential pattern analysis, including both sequential pattern modelling and mining. Due to space limitation, this submission presents mainly the 'temporal skeletonization', our approach to identifying the meaningful granularity for sequential pattern mining. Our 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. In the meantime, the embedding topology of the graph allows us to translate the rich temporal content into a metric space. This opens up new possibilities to explore, quantify, and visualize sequential data. Our approach has shown to provide substantial improvements over the state-of-the-art methods in challenging tasks of sequential pattern mining and sequence clustering. Evaluation on a Business-to-Business (B2B) marketing application demonstrates that our approach can effectively discover critical buying paths from noisy customer event data.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
EditorsZhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherIEEE Computer Society
Number of pages7
ISBN (Electronic)9781479942749
StatePublished - Jan 26 2015
Event14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China
Duration: Dec 14 2014 → …

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259


Conference14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
Period12/14/14 → …

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software


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