Applications of Knowledge Discovery
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Knowledge Discovery from Databases (KDD) – also named Data Mining – is a growing field since 10 years which combines techniques from databases, statistics, and machine learning. Applications of KDD most often have one of the following goals:
– Customer relationship management: who are the best customers, which products are to be offered to which customers (direct marketing or customer acquisition), which customers are likely to end the relationship (customer churn), which customers are likely to not pay (also coined as fraud detection)?
– Decision support applies to almost all areas, ranging from medicine over marketing to logistics. KDD applications aim at a data-driven justification of decisions by relating actions and outcomes.
– Recommender systems rank objects according to user profiles. The objects can be, for instance, products as in the amazon internet shop, or documents as in learning search engines. KDD applications do not assume user profiles to be given but learns tehm from observations of user behavior.
– Plant asset management moves beyond job scheduling and quality control. The goal is to optimize the overall benefits of production.
KeywordsKnowledge Discovery Recommender System Customer Relationship Management Concept Drift Audio Data
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- 1.Agrawal, G.: High-level interfaces for data mining: From offline algorithms on clusters to streams on grids. In: Workshop on Data Mining and Exploration Middleware for Distributed and Grid Computing, Minneapolis, MN (September 2003)Google Scholar
- 2.Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: Crisp–Dm 1.0. Technical report, The CRISP–DM Consortium (August 2000)Google Scholar
- 3.Chudzian, C., Granat, J., Traczyk, W.: Call Center Case. Deliverable D17.2b, IST Project MiningMart, IST-11993 (2003)Google Scholar
- 4.Granat, J., Traczyk, W., Chudzian, C.: Evaluation report by NIT. Deliverable D17.3b, IST Project MiningMart, IST-11993 (2003)Google Scholar
- 5.Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams: Theory and practice. IEEE Transaction Knowledge Data Engineering 15(3) (2003)Google Scholar
- 6.Kargupta, H., Chan, P.: Distributed data mining. AI Magazine 20(1), 126 (1999)Google Scholar
- 7.Keogh, E., Lonardi, S., Chiu, B.: Finding suprising patterns in a time series database in linear time and space. In: Procs. Int. Conf. on Knowledge Discovery in Databases (2002)Google Scholar
- 10.Morik, K., Scholz, M.: The MiningMart Approach to Knowledge Discovery in Databases. In: Zhong, N., Liu, J. (eds.) Intelligent Technologies for Information Analysis. Springer, Heidelberg (2004)Google Scholar
- 11.Richeldi, M., Perrucci, A.: Churn analysis case study. Deliverable D17.2, IST Project MiningMart, IST-11993 (2002)Google Scholar
- 12.Richeldi, M., Perrucci, A.: Mining Mart Evaluation Report. Deliverable D17.3, IST Project MiningMart, IST-11993 (2002)Google Scholar
- 13.Ritthoff, O., Klinkenberg, R., Fischer, S., Mierswa, I.: A hybrid approach to feature selection and generation using an evolutionary algorithm. In: Bullinaria, J.A. (ed.) Proceedings of the 2002 U.K. Workshop on Computational Intelligence (UKCI 2002), Birmingham, UK, University of Birmingham, September 2002, pp. 147–154 (2002)Google Scholar