Modeling preparation for data mining processes

• Autorzy: Eule T.
• Języki publikacji: EN

Abstrakty

EN

Today many different software tools for decision support exist; the same is true for data mining which can be seen as a particularly challenging sub-area of decision support. Choosing the most suitable tool for a particular industrial data mining application is becoming difficult, especially for industrial decision makers whose expertise is in a different field. This paper provides a conceptual analysis of crucial features of current data mining software tools, by establishing an abstract view on typical processes in data mining. Thus a common terminology is given which simplifies the comparison of tools. Based on this analysis, objective decisions for the application of decision supporting software tools in industrial practice can be made.

Słowa kluczowe

EN

data mining; data preparation; KDD process

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2006
• Tom: nr 4
• Strony: 81--87
• Opis fizyczny: Bibliogr. 20 poz.

Twórcy

autor

Eule T.

• Computer Science VIII, University of Dortmund, D-44221 Dortmund, Germany,

Bibliografia

• [1] D. W. Abbott, I. P. Matkovsky, and J. F. Elder IV, “An evaluation of high-end data mining tools for fraud detection”, in IEEE Int. Conf. Syst., Man. Cybern., San Diego, USA, 1998.
• [2] S. AlSairafi, F.-S. Emmanouil, M. Ghanem, N. Giannadakis, Y. Guo, D. Kalaitzopoulos, M. Osmond, A. Rowe, J. Syed, and P. Wendel, “The design of discovery net: towards open grid services for knowledge discovery”, High-Perform. Comput. Appl., vol. 17, no. 3, pp. 297–315, 2003.
• [3] A. V. Aho and J. D. Ullman, “Universality of data retrieval languages”, in Proc. 6th ACM Symp. Princ. Programm. Languag., San Antonio, USA, 1979, pp. 110–117.
• [4] A. Bernstein, S. Hill, and F. Provost, “Toward intelligent assistance for a data mining process: an ontology-based approach for costsensitive classification”, IEEE Trans. Knowl. Data Eng., vol. 17, no. 4, pp. 503–518, 2005.
• [5] M. Cannataro, A. Congiusta, C. Mastroianni, A. Pugliese, D. Talia, and P. Trunfio, “Grid-based data mining and knowledge discovery”, in Intelligent Technologies for Information Analysis, N. Zhong and J. Liu, Eds. Berlin: Springer, 2004.
• [6] H. Cespivova, J. Rauch, V. Svatek, M. Kejkula, and M. Tomeckova, “Roles of medical ontology in association mining CRISP-DM cycle”, in Worksh. Knowl. Discov. Ontol. ECML/PKDD, Pisa, Italy, 2004.
• [7] P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth, “CRISP–DM 1.0.”, Tech. Rep., The CRISP–DM Consortium, Aug. 2000.
• [8] K. W. Collier, D. Sautter, C. Marjaniemi, and B. Carey, “A methodology for evaluating and selecting data mining software”, in Proc. 32nd Hawaii Int. Conf. Syst. Sci. HICSS-32, Hawaii, USA, 1999.
• [9] T. Euler and M. Scholz, “Using ontologies in a KDD workbench”, in Worksh. Knowl. Discov. Ontol. ECML/PKDD, Pisa, Italy, 2004.
• [10] Ch. G. Giraud-Carrier and O. Povel, “Characterising data mining software”, Intell. Data Anal., vol. 7, no. 3, pp. 181–192, 2003.
• [11] M. Goebel and L. Gruenwald, “A survey of data mining and knowledge discovery software tools”, ACM SIGKDD Explor., vol. 1, no. 1, pp. 20–33, 1999.
• [12] T. R. Gruber, “Towards principles for the design of ontologies used for knowledge sharing”, in Formal Ontology in Conceptual Analysis and Knowledge Representation, N. Guarino and R. Poli, Eds. Deventer: Kluwer, 1993.
• [13] M. F. Hornick, H. Yoon, and S. Venkayala, “Java data mining (JSR-73): status and overview”, in Proc. Worksh. Data Min. Stand., Serv. Platf. 10th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (KDD), Seattle, USA, 2004, pp. 23–29.
• [14] J.-U. Kietz, A. Vaduva, and R. Z¨ucker, “Mining mart: combining case-based-reasoning and multi-strategy learning into a framework to reuse KDD-application”, in Proc. Fifth Int. Worksh. Multistrat. Learn. (MSL2000), Guimares, Portugal, 2000.
• [15] I. Mierswa, R. Klinkberg, S. Fischer, and O. Ritthoff, “A flexible platform for knowledge discovery experiments: YALE – yet another learning environment”, in LLWA 03 Tagung. GI-Worksh. Woche Lern. Leh. Wiss. Adapt., Karlsruhe, Germany, 2003.
• [16] K. Morik and M. Scholz, “The miningmart approach to knowledge discovery in databases”, in Intelligent Technologies for Information Analysis, N. Zhong and J. Liu, Eds. Berlin: Springer, 2004.
• [17] D. Pyle, Data Preparation for Data Mining. San Francisco: Morgan Kaufmann, 1999.
• [18] S. Raspl, “PMML Version 3.0 – overview and status”, in Proc. Worksh. Data Min. Stand., Serv. Platf. 10th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (KDD), Seattle, USA, 2004, pp. 18–22.
• [19] G. J. Williams and Z. Huang, “Modelling the KDD process”, Tech. Rep. TR-DM-96013, Commonwealth Scientific and Industrial Research Organisation (CSIRO), DIT Data Mining, 1996.
• [20] N. Zhong, C. Liu, and S. Ohsuga, “Dynamically organizing KDD processes”, Int. J. Patt. Recogn. Artif. Intell., vol. 15, no. 3, pp. 451–473, 2001.