TWave: High-order analysis of spatiotemporal data

Michael Barnathan, Vasileios Megalooikonomou, Christos Faloutsos, Feroze B. Mohamed, Scott Faro

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

Abstract

Recent advances in data acquisition and sharing have made available large quantities of complex data in which features may have complex interrelationships or may not be scalar. For such datasets, the traditional matrix model is no longer appropriate and may fail to capture relationships between features or fail to discover the underlying concepts that features represent. These datasets are better modeled using tensors, which are high-order generalizations of matrices. However, naive tensor algorithms suffer from poor efficiency and may fail to consider spatiotemporal neighborhood relationships in analysis. To surmount these difficulties, we propose TWave, a wavelet and tensor-based methodology for automatic summarization, classification, concept discovery, clustering, and compression of complex datasets. We also derive TWaveCluster, a novel high-order clustering approach based on WaveCluster, and compare our approach against WaveCluster and k-means. The efficiency of our method is competitive with WaveCluster and significantly outperforms k-means. TWave consistently outperformed competitors in both speed and accuracy on a 9.3 GB medical imaging dataset. Our results suggest that a combined wavelet and tensor approach such as TWave may be successfully employed in the analysis of complex high-order datasets.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
Pages246-253
Number of pages8
EditionPART 1
DOIs
StatePublished - Dec 1 2010
Event14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, India
Duration: Jun 21 2010Jun 24 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6118 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
CountryIndia
CityHyderabad
Period6/21/106/24/10

Keywords

  • Matrix models
  • Spatiotemporal mining
  • Tensors
  • Wavelets

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Barnathan, M., Megalooikonomou, V., Faloutsos, C., Mohamed, F. B., & Faro, S. (2010). TWave: High-order analysis of spatiotemporal data. In Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings (PART 1 ed., pp. 246-253). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6118 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-13657-3_28