![]() ![]() Schommer, Christoph ![]() in Abstract book of 5th Recent Advances in Soft Computing (RASC 2004) (2004) Detailed reference viewed: 47 (1 UL)![]() ![]() ; Schommer, Christoph ![]() in Proceedings KI 2004 (2004) Detailed reference viewed: 212 (0 UL)![]() ![]() Schommer, Christoph ![]() in Workshop on Symbolic Networks. ECAI 2004 (2004) Detailed reference viewed: 72 (13 UL)![]() ![]() Schroeder, Ben ![]() ![]() in Workshop Knowledge Discovery in Data Streams. ECML/PKDD 2005 (2004) Detailed reference viewed: 104 (0 UL)![]() ![]() Schommer, Christoph ![]() in Proceedings 2004 International Conference on Advances in Intelligent Systems - Theory and Applications (2004) Detailed reference viewed: 51 (1 UL)![]() Schommer, Christoph ![]() Book published by Shaker Publishing (2003) Detailed reference viewed: 94 (3 UL)![]() ![]() Bayerl, Stephan ![]() ![]() in Data Mining III, 6 (2002) “Scoring”, in general, is defined as the usage of mining models - based on historical data - for classification or segmentation of new items. For example: if the historical data consist of classified ... [more ▼] “Scoring”, in general, is defined as the usage of mining models - based on historical data - for classification or segmentation of new items. For example: if the historical data consist of classified customers, then we can use the model for the prediction of the behaviour of a new customer. Scoring offers novel ways to exploit the power of data mining models in everyday business activities, and proliferate mining applications to users who are not educated in mining. In this paper, we present a) the generic scoring process b) its technical mplementation, and c) an example of how scoring can be integrated in a real application. The generic process consists of three steps: The mining models are learned first, then they are transferred into the application database, and finally the models are applied to the data loaded in that database. Arguments for the necessity of such a mining improvement are collected. IBM DB2 Intelligent Miner Scoring (IM Scoring) is the first technical implementation of scoring. It is based on the emerging open-standard for mining models (Predictive Model Markup Language - PMML), and the mining extensions for SQL. Implementation issues are discussed, as well as problems that come along with its integration into operational applications. The article closes with the description of a sample application, the integration of scoring into a call center environment. A discussion of the scoring method concludes this article. [less ▲] Detailed reference viewed: 55 (2 UL)![]() Andersen, Christian ![]() ![]() Book published by IBM Press (2001) Detailed reference viewed: 56 (0 UL)![]() Andersen, Christian ![]() ![]() Book published by IBM Press (2001) Detailed reference viewed: 47 (0 UL)![]() ![]() Müller, Ulrike ![]() ![]() in HMD - Praxis der Wirtschaftsinformatik, 06/2001 (2001) Detailed reference viewed: 201 (6 UL)![]() Andersen, Christian ![]() ![]() Book published by IBM Press (2001) Detailed reference viewed: 80 (2 UL)![]() Andersen, Christian ![]() ![]() Book published by IBM Press (2001) Detailed reference viewed: 50 (0 UL)![]() Schommer, Christoph ![]() Book published by AKA Publishing (2001) Detailed reference viewed: 48 (1 UL)![]() ![]() ; Schommer, Christoph ![]() in Proceedings of the World Congress on Neural Networks (1993) Detailed reference viewed: 61 (2 UL) |
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