Fast mining of massive tabular data via approximate distance computations

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 L/sub p/ norms. A novelty of our distance computation procedures is that they work for any L/sub p/ norms, not only the traditional p = 2 or p = 1, but for all p /spl les/ 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.