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 @techreport{dp2009-05,
 	Abstract = {Software development effort estimation is important for quality management in the software development industry, yet its automation still remains a challenging issue. Applying machine learning algorithms alone often can not achieve satisfactory results. In this paper, we present an integrated data mining framework that incorporates domain knowledge into a series of data analysis and modeling processes, including visualization, feature selection, and model validation. An empirical study on the software effort estimation problem using a benchmark dataset shows the effectiveness of the proposed approach.},
 	Address = {Dunedin, New Zealand},
-	Author = {Jeremiah D. Deng and Martin K. Purvis},
+	Author = {Jeremiah D. Deng and Martin K. Purvis and Maryam A. Purvis},
 	Date-Added = {2009-06-07 21:50:57 +1200},
-	Date-Modified = {2009-06-08 15:02:18 +1200},
+	Date-Modified = {2009-06-08 16:55:56 +1200},
 	Institution = {Department of Information Science, University of Otago},
 	Keywords = {software effort estimation, machine learning},
 	Month = jun,
 	Number = {05},
-	Size = {184 KB},
+	Size = {260 KB},
 	Title = {Software effort estimation: Harmonizing algorithms and domain knowledge in an integrated data mining approach},
 	Type = {Discussion paper},
 	Year = {2009}}