Temporal skeletonization on sequential data: Patterns, categorization, and visualization

Chuanren Liu, Kai Zhang, Hui Xiong, Geoff Jiang, Qiang Yang

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

19 Scopus citations

Abstract

Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in a sequence. With the growing complexity of real-world dynamic scenarios, more and more symbols are often needed to encode a meaningful sequence. This is so-called 'curse of cardinality', which can impose significant challenges to the design of sequential analysis methods in terms of computational efficiency and practical use. Indeed, given the overwhelming scale and the heterogeneous nature of the sequential data, new visions and strategies are needed to face the challenges. To this end, in this paper, we propose a 'temporal skeletonization' approach to proactively reduce the representation of sequences to uncover significant, hidden 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. 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 greatly alleviate the curse of cardinality in challenging tasks of sequential pattern mining and 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 publicationKDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1336-1345
Number of pages10
ISBN (Print)9781450329569
DOIs
StatePublished - 2014
Event20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014 - New York, NY, United States
Duration: Aug 24 2014Aug 27 2014

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014
Country/TerritoryUnited States
CityNew York, NY
Period8/24/148/27/14

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Keywords

  • curse of cardinality
  • network embedding
  • sequential pattern mining
  • temporal skeletonization

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