Two heads better than one: pattern discovery in time-evolving multi-aspect data |
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Authors: | Jimeng Sun Charalampos E. Tsourakakis Evan Hoke Christos Faloutsos Tina Eliassi-Rad |
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Affiliation: | (1) IBM TJ Watson Research Center, Hawthorne, NY, USA;(2) Carnegie Mellon University, Pittsburgh, PA, USA;(3) Apple Computer, Inc., Cupertino, CA, USA;(4) Lawrence Livermore National Laboratory, Livermore, CA, USA |
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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. |
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Keywords: | Tensor Multilinear analysis Stream mining Wavelet |
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