Fast Mining of Massive Tabular Data via Approximate Distance Computations Graham Cormode, Piotr Indyk, Nick Koudas, and S. Muthukrishnan   View Paper (PDF)   Return to Session 15: Data, Text and Web Mining-2 Abstract Tabular data abound in many data stores: traditional relational databases store tables, and new applications also generate massive tabular datasets. We present methods for determining similar regions in massive tabular data. Our methods are for computing the "distance" between any two subregions of 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 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 ATT'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. DiSC'03 © 2003 Association for Computing Machinery