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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References
Agrawal, R., Imielinski, T., Swami, A.: Database Mining: A performance Perspective. IEEE Trans. Knowledge and Data Engineering 5(6), 914–925 (1993b)
Agrawal, R., Imielinski, T., Swami, A.: Mining associations between sets of items in large databases. In: Proc. of ACM SIGMOD Int. Conference on Management of Data, Washinton, D.C., pp. 207–216 (1993a)
Agrawal, R., Srikant, R.: Fast Algorithms for mining association rules in large databases. In: Proc. of 20th Int. Conference on Very Large Databases, pp. 487–489. Santiago de Chile (1994)
Albrecht, A.J.: Measuring Application Development: Proc. IBM Applications Development Joint SHARE/GUIDE Symposium, Monterey, CA, pp. 83-92 (1979)
Bayardo, R., Agrawal, R., Gunopulos, D.: Constraint-based rule mining in large, dense database. In: Proc. 15th Int. Conference on Data Engineering, pp. 188–197 (1999)
Bayardo, R., Agrawal, R.: Constraint-based rule mining in large, dense database. In: Proc. Mining the most interesting rules. Proc. ACM SIGKDD Int. Conf. Knowledge Discovery in Databases, pp. 145–154. ACM Press, New York (1999)
Boehm, B.W., Clark, B., Horowitz, E., et al.: Cost Models for Future Life Cycle Processes: COCOMO 2.0. Annals Software Engineering 1(1), 1–24 (1995)
Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., Zanasi, A.: Discovering Data Mining. from concept to implementation. Prentice-Hall, Englewood Cliffs (1998)
Dolado, J.J.: A Validation of the Ccomponent-Based Method for Software Size Estimation. IEEE Transactions on Software Engineering 26(10), 1006–1021 (2000)
Hall, B., Orr, G., Reeves, T.E.: A Technique for Function Block Counting. The Journal of System and Software 57, 217–220 (2001)
Hu, Y.C., Chen, R.S., Tzeng, G.H.: Mining fuzzy associative rules for classifications problems. Computers and Industrial Engineering
Li, J., Shen, H., Topor, R.: Mining the smallest association rule set for predictions. In: Proc. IEEE International Conference on Data Mining, ICDM 2001 (2001)
Liu, B., Hsu, W., Ma, Y.: Integration Classification and Association Rule Mining. In: Proc. 4th Int. Conference on Knowledge Discovery and Data Mining, pp. 80–86 (1998)
Liu, B., Hsu, W., Chen, S., Ma, Y.: Analyzing the subjective Interestingness of Association Rules. IEEE Intelligent Systems, 47-55 (September/October 2000)
Mineset user’s guide, v. 007-3214-004, 5/98 (Silicon Graphics 1998)
Moreno, M.N., Miguel, L.A., García, F.J., Polo, M.J.: Data mining approaches for early software size estimation. In: Proc. 3rd ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2002), Madrid, Spain, pp. 361–368 (2002)
Moreno, M.N., Miguel, L.A., García, F.J., Polo, M.J.: Building knowledge discoverydriven models for decision support in project management. Dec. Support Syst. (in press)
Padmanabhan, B., Tuzhilin, A.: Knowledge refinement based on the discovery of unexpected patterns in data mining. Decision Support Systems 27, 303–318 (1999)
Padmanabhan, B., Tuzhilin, A.: Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems 33, 309–321 (2002)
Symons, C.R.: Software Sizing and Estimating MKII FPA. John Wiley and Sons, Chichester (1991)
Verner, J., Tate, G.: A Software Size Model. IEEE Transaction of Software Engineering 18(4), 265–278 (1992)
Wang, Y., Wong, A.K.C.: From Association to Classification: Inference Using Weight of Evidence. IEEE Transactions on Knowledge and Data Engineering 15, 764–767 (2003)
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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
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