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2013 IEEE 29th International Conference on Data Engineering (ICDE) (2002)
San Jose, California
Feb. 26, 2002 to Mar. 1, 2002
ISBN: 0-7695-1531-2
pp: 0605
Nick Koudas , AT&T Research
S. Muthukrishnan , AT&T Research
Graham Cormode , University of Warwick
Tabular data abound in many data stores: traditional relational databases store tables, and new applications also generate massive tabular datasets. For example, consider the geographic distribution of cell phone traffic at different base stations across the country or the evolution of traffic at Internet routers over time.Detecting similarity patterns in such data sets (e.g., which geographic regions have similar cell phone usage distribution, which IP subnet traffic distributions over time intervals are similar, etc) is of great importance. Identification of such patterns poses many conceptual challenges (what is a suitable similarity distance function for two ``regions'') as well as technical challenges (how to perform similarity computations efficiently as massive tables get accumulated over time) that we address.We present methods for determining similar regions in massive tabular data. Our methods are for computing the ``distance'' between any two subregions of a tabular data: they are approximate, but highly accurate as we prove mathematically, and they are fast, running in time nearly linear in the table size. Our methods are general since these distance computations can be applied to any mining or similarity algorithms that use Lp norms. A novelty of our distance computation procedures is that they work for any Lp norms --- not only the traditional p=2 or p=1, but for all p <= 2; the choice of p, say, fractional p, provides an interesting alternative similarity behavior!We use our algorithms in a detailed experimental study of the clustering patterns in real tabular data obtained from one of AT&T's data stores and show that our methods are substantially faster than straightforward methods while remaining highly accurate, and able to detect interesting patterns by varying the value of p.
Approximation, Clustering, Data Mining, Euclidean Distance, Manhattan Distance, Lp norms, Tabular Data.
Piotr Indyk, Nick Koudas, S. Muthukrishnan, Graham Cormode, "Fast Mining of Massive Tabular Data via Approximate Distance Computations", 2013 IEEE 29th International Conference on Data Engineering (ICDE), vol. 00, no. , pp. 0605, 2002, doi:10.1109/ICDE.2002.994778
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