Data Cleaning publication categorizer

Many real-world KDD expeditions involve investigation of relationships between variables in
different, heterogeneous databases. We present
a dynamic programming technique for linking
records in multiple heterogeneous databases using loosely defined fields that allow free-style verbatim entries. We develop an interestingness
measure based on non-parametric randomization
tests, which can be used for mining potentially
useful relationships among variables. This mea-
sure uses distributional characteristics of historical events, hence accommodating variable-length
records in a natural way. As an illustration, we
include a successful application of the proposed
methodology to a real-world data mining problem
at Lucent Technologies.