Multivariable stream data classification using motifs and their temporal relations

Sungbo Seo, Jaewoo Kang, Keun Ho Ryu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Multivariable stream data is becoming increasingly common as diverse types of sensor devices and networks are deployed. Building accurate classification models for such data has attracted a lot of attention from the research community. Most of the previous works, however, relied on features extracted from individual streams, and did not take into account the dependency relations among the features within and across the streams. In this work, we propose new classification models that exploit temporal relations among features. We showed that consideration of such dependencies does significantly improve the classification accuracy. Another benefit of employing temporal relations is the improved interpretability of the resulting classification models, as the set of temporal relations can be easily translated to a rule using a sequence of inter-dependent events characterizing the class. We evaluated the proposed scheme using different classification models including the Naive Bayesian, TFIDF, and vector distance models. We showed that the proposed model can be a useful addition to the set of existing stream classification algorithms.

Original languageEnglish
Pages (from-to)3489-3504
Number of pages16
JournalInformation Sciences
Issue number20
Publication statusPublished - 2009 Sept 29

Bibliographical note

Funding Information:
The major part of the work done by Seo and Kang was conducted at Kang’s Lab, North Carolina State University, while they were with the NCSU. This work was partially supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2007-359-D00015), the Korea Science and Engineering Foundation (KOSEF) Grant funded by the Korean government (MEST) (R01-2008-000-20564-0, R01-2007-000-10926-0, and R11-2008-014-02002-0), the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2009-0077688), the Second Brain Korea 21 Project Grant, Grant (#07KLSGC02) from the Cutting-edge Urban Development – Korean Land Spatialization Research Project funded by the Ministry of Construction & Transportation of the Korean government, and the Korea Research Foundation Grant funded by the Korean Government(MEST, The Regional Core Research Program/Chungbuk BIT Research-Oriented University Consortium).


  • Data classification
  • Motifs
  • Multivariable stream
  • Stream data mining
  • Stream data modeling
  • Temporal relations

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Control and Systems Engineering
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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