By Krzysztof J Cios; et al
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Additional resources for Data mining : a knowledge discovery approach
1. org/. 22 5. 6. Application of PMML and XML standards in the framework of the KDP model. development of new and improved data mining algorithms rather than on research to support other knowledge discovery activities. – The second-generation systems, called suites, were developed in the mid-1990s. They provided multiple types of integrated data analysis methods, as well as support for data cleaning, preprocessing, and visualization. Examples include systems like SPSS’s Clementine® , Silicon Graphics’s MineSet™, IBM’s Intelligent Miner, and SAS Institute’s Enterprise Miner.
Chapter 2 The Knowledge Discovery Process 21 – The main reason for introducing process models is to formalize knowledge discovery projects within a common framework, a goal that will result in cost and time savings, and will improve understanding, success rates, and acceptance of such projects. The models emphasize independence from specific applications, tools, and vendors. , and the CRISP-DM model were introduced. Each model has its strong and weak points, based on its application domain and particular business objectives.
International Journal of Intelligent Agents, 7(7):675–686 16. Piatesky-Shapiro, G. 1999. The data mining industry coming to age. IEEE Intelligent Systems, 14(6): 32–33 17. Shearer, C. 2000. The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing, 5(4):13–19 18. , and Kerber, R. 1994. Integrating inductive and deductive reasoning for data mining. Proceedings of 1994 AAAI Workshop on Knowledge Discovery in Databases, 37–48, Seattle, Washington, USA 19. , and Hipp, J. 2000.