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In this project, w e dev eloped a technique for extracting useful information from databases that contain both xedformat and free-text elds. The present state of the art in data mining is a schism betw een tec hniques that handle only xed-format data (pattern recognition, classi cation algorithms from machine learning), and techniques designed for free-form text (information retrieval). Advanced knowledge disco very technologies ha ve been developed in both research areas, but systems that can categorize or cluster records containing both kinds of data are still lacking. Speci cally, we examined database records from a Honeywell service cen ter to extract information about the expected cost of di erent kinds of service requests. Our goal was to test the h ypothesis that incorporating information from free-text elds would provide a better categorization of these records; in this case, better predictions of the cost of the service call. In our w ork, we have integrated feature extraction and clustering techniques from information retrieval with classi cation algorithms from machine learning in order to categorize the hybrid elds. Our preliminary results suggested that incorporating free-form text could potentially induce better classi cation models.

Textual data mining of service center call records