Data Mining and Knowledge Discovery

, Volume 17, Issue 1, pp 111–128

Two heads better than one: pattern discovery in time-evolving multi-aspect data


    • IBM TJ Watson Research Center
  • Charalampos E. Tsourakakis
    • Carnegie Mellon University
  • Evan Hoke
    • Apple Computer, Inc.
  • Christos Faloutsos
    • Carnegie Mellon University
  • Tina Eliassi-Rad
    • Lawrence Livermore National Laboratory

DOI: 10.1007/s10618-008-0112-3

Cite this article as:
Sun, J., Tsourakakis, C.E., Hoke, E. et al. Data Min Knowl Disc (2008) 17: 111. doi:10.1007/s10618-008-0112-3


Data stream values are often associated with multiple aspects. For example each value observed at a given time-stamp from environmental sensors may have an associated type (e.g., temperature, humidity, etc.) as well as location. Time-stamp, type and location are the three aspects, which can be modeled using a tensor (high-order array). However, the time aspect is special, with a natural ordering, and with successive time-ticks having usually correlated values. Standard multiway analysis ignores this structure. To capture it, we propose 2 Heads Tensor Analysis (2-heads), which provides a qualitatively different treatment on time. Unlike most existing approaches that use a PCA-like summarization scheme for all aspects, 2-heads treats the time aspect carefully. 2-heads combines the power of classic multilinear analysis with wavelets, leading to a powerful mining tool. Furthermore, 2-heads has several other advantages as well: (a) it can be computed incrementally in a streaming fashion, (b) it has a provable error guarantee and, (c) it achieves significant compression ratio against competitors. Finally, we show experiments on real datasets, and we illustrate how 2-heads reveals interesting trends in the data. This is an extended abstract of an article published in the Data Mining and Knowledge Discovery journal.


TensorMultilinear analysisStream miningWavelet

Copyright information

© Springer Science+Business Media, LLC 2008