Data Mining – Ein neues Paradigma der angewandten Statistik
DOI:
https://doi.org/10.17713/ajs.v31i1.469Abstract
Im Rahmen dieses Beitrages wird versucht die Unterschiede im methodologischen Ansatz bei Verfahren des Data Mining den traditionellen statistischen Modellierungskonzepten gegenüberzustellen. Abschließend wird auf die allgemeinen Grenzen und Probleme bei derAnwendung von Methoden des Data Mining eingegangen.
References
J.D. Banfield and A.E. Raftery. Model based Gaussian and non-Gaussian clustering. Biometrics, 49:803-821, 1993.
D. Coleman, X. Dong, J. Hardin, D.M. Rocke, and D.L. Woodruff. Some computational issues in cluster analysis with no a priori metric. Comp. Stat. and Data Analysis, 31:1-11, 1999.
U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, 1996.
U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery in databases. AI Magazine, 37-54, 1996.
C. Glymour, D. Madigan, D. Pregibon, and P. Smyth. Statistical inference and data mining. CACM, 39:35-41, 1996.
M. Goebel and L. Gruenwald. A survey of data mining and knowledge discovery software tools. SIGKDD Explorations, 1:20-33, 1999.
D.J. Hand. Why data mining is more than statistics writ large. In Bulletin of the International Statistical Institute ISI 99, Proceedings 52nd Session, pages 433-436, 1999.
M. Hudec and P.M. Steiner. Model-based classification of large data sets. To appear COMPSTAT 2002.
W.H. Inmon. The data warehouse and data mining. CACM, 39: 49-50, 1996. J.R. Kettenring. Shaping statistics for success in the 21st century. JASA, 92:1229-1234, 1997.
T. Lim and W. Loh. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40(3):203-228, 2000
R.S. Michalski and R.E. Stepp. Learning from observation; Conceptual clustering. In M. Kaufmann, editor, Machine Learning: An Artificial Intelligence Approach. pages 331-363, 1983.
D. Michie, D.J. Spiegelhalter, and C.C. Taylor. Machine Learning, Neural and Statistical Classification. Englewood Cliffs, N.J, 1994.
J.W. Tukey. The future of data analysis. Annals of Mathematical Statistics 33:1-67, 1962
D.L: Woodruff and D.M. Rocke. Computable robust estimation of multivariate location and shape in high dimension using compound estimators. JASA, 89:888-896, 1994.
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