Beyond streams and graphs: Dynamic tensor analysis

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Beyond streams and graphs: Dynamic tensor analysis (2006)

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by Jimeng Sun , Dacheng Tao , Christos Faloutsos

Venue:	In KDD
Citations:	113 - 16 self

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@INPROCEEDINGS{Sun06beyondstreams,
    author = {Jimeng Sun and Dacheng Tao and Christos Faloutsos},
    title = {Beyond streams and graphs: Dynamic tensor analysis},
    booktitle = {In KDD},
    year = {2006},
    pages = {374--383}
}

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Abstract

How do we find patterns in author-keyword associations, evolving over time? Or in DataCubes, with product-branchcustomer sales information? Matrix decompositions, like principal component analysis (PCA) and variants, are invaluable tools for mining, dimensionality reduction, feature selection, rule identification in numerous settings like streaming data, text, graphs, social networks and many more. However, they have only two orders, like author and keyword, in the above example. We propose to envision such higher order data as tensors, and tap the vast literature on the topic. However, these methods do not necessarily scale up, let alone operate on semi-infinite streams. Thus, we introduce the dynamic tensor analysis (DTA) method, and its variants. DTA provides a compact summary for high-order and high-dimensional data, and it also reveals the hidden correlations. Algorithmically, we designed DTA very carefully so that it is (a) scalable, (b) space efficient (it does not need to store the past) and (c) fully automatic with no need for user defined parameters. Moreover, we propose STA, a streaming tensor analysis method, which provides a fast, streaming approximation to DTA. We implemented all our methods, and applied them in two real settings, namely, anomaly detection and multi-way latent semantic indexing. We used two real, large datasets, one on network flow data (100GB over 1 month) and one from DBLP (200MB over 25 years). Our experiments show that our methods are fast, accurate and that they find interesting patterns and outliers on the real datasets. 1.

Keyphrases

dynamic tensor analysis beyond stream multi-way latent semantic indexing space efficient invaluable tool real datasets social network streaming tensor analysis method principal component analysis high-dimensional data dimensionality reduction anomaly detection product-branchcustomer sale information large datasets matrix decomposition semi-infinite stream rule identification order data interesting pattern compact summary numerous setting hidden correlation author-keyword association vast literature feature selection real setting network flow data

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