document: Learning to match and cluster large high-dimensional data sets for data integration Part of the process of data integration is determining which sets of identifiers refer to the same real-world entities . In integrating databases found on the Web or obtained by using information extraction methods , it is often possible to solve this problem by exploiting similarities in the textual names used for objects in different databases . In this paper we describe techniques for clustering and matching identifier names that are both scalable and adaptive , in the sense that they can be trained to obtain better performance in a particular domain . An experimental evaluation on a number of sample datasets shows that the adaptive method sometimes performs much better than either of two non-adaptive baseline systems , and is nearly always competitive with the best baseline system .
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