Article

Data Mining and Knowledge Discovery

, Volume 17, Issue 1, pp 111-128

First online:

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

  • Jimeng SunAffiliated withIBM TJ Watson Research Center Email author 
  • , Charalampos E. TsourakakisAffiliated withCarnegie Mellon University
  • , Evan HokeAffiliated withApple Computer, Inc.
  • , Christos FaloutsosAffiliated withCarnegie Mellon University
  • , Tina Eliassi-RadAffiliated withLawrence Livermore National Laboratory

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Abstract

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.

Keywords

Tensor Multilinear analysis Stream mining Wavelet