@incollection{Dolado2007,
abstract = {One of the problems found in generic project databases, where the data is collected from different organizations, is the large disparity of its instances. In this chapter, we characterize the database selecting both attributes and instances so that project managers can have a better global vision of the data they manage. To achieve that, we first make use of data mining algorithms to create clusters. From each cluster, instances are selected to obtain a final subset of the database. The result of the process is a smaller database which maintains the prediction capability and has a lower number of instances and attributes than the original, yet allow us to produce better predictions.},
author = {Dolado, J.J. and Rodriguez, D. and Riquelme, J. and Ferrer-Troyano, F. and Cuadrado, J.J.},
booktitle = {Advances in Machine Learning Applications in Software Engineering},
doi = {10.4018/978-1-59140-941-1.ch001},
editor = {Zangh, D and Tsai, J},
isbn = {9781591409410},
keywords = {ISBSG,Least Median Squares,Linear Regression,MMRE,Zone Regression},
pages = {1--13},
publisher = {IGI Global},
title = {A two-stage zone regression method for global characterization of a project database},
url = {https://www.igi-global.com/chapter/two-stage-zone-regression-method/4854},
year = {2007}
}