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 (PARAFAC [1], Tucker [5], DTA/STA [3], WTA [2]) 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 [4].
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References
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Sun, J., Tsourakakis, C.E., Hoke, E., Faloutsos, C., Eliassi-Rad, T.: Two heads better than one: Pattern discovery in time-evolving multi-aspect data. Data Mining and Knowledge Discovery 17(1), 111–128 (August 2008)
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Sun, J., Tsourakakis, C.E., Hoke, E., Faloutsos, C., Eliassi-Rad, T. (2008). Two Heads Better Than One: Pattern Discovery in Time-Evolving Multi-aspect Data . In: Daelemans, W., Goethals, B., Morik, K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science(), vol 5211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87479-9_19
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DOI: https://doi.org/10.1007/978-3-540-87479-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87478-2
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