Abstract
Association and classification are two data mining techniques traditionally used for solving different kind of problems. Association has been applied in knowledge discovery and classification in predictive tasks. Recent studies have shown that knowledge discovery algorithms can be successfully used for prediction in classification problems. The improvement of association rules algorithms is the subject of many works in the literature, whereas little research has been done concerning their classification aspect. On the other hand, methods for solving the problems of the association rules must be tailored to the particularities of the application domain. This work deals with the predictive use of association rules and addresses the problem of reducing the number of rules generated in the software project management field. We propose an algorithm for refining association rules based on incremental knowledge discovery. It provides managers with strong rules for decision making without need of domain knowledge.
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García, M.N.M., Peñalvo, F.J.G., Martín, M.J.P. (2004). Mining Interesting Association Rules for Prediction in the Software Project Management Area. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2004. Lecture Notes in Computer Science, vol 3181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30076-2_34
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DOI: https://doi.org/10.1007/978-3-540-30076-2_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22937-7
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