UNI / FIN / IKS Arbeitsgruppe Computational Intelligence |
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Mirko Böttcher
Journal ArticlesMirko Böttcher, Martin Spott, Detlef Nauck, and Rudolf Kruse. Mining changing customer segments in dynamic markets. Expert Systems with Applications, 36(1):155-164, 2009 Mirko Böttcher, Myra Spiliopoulou, and Frank Höppner. On exploiting the power of time in data mining. SIGKDD Explorations Newsletter, 10(2):3-11, 2008 Mirko Böttcher, Detlef Nauck, Christian Borgelt, and Rudolf Kruse. A framework for discovering interesting business changes from data. BT Technology Journal, 24(3):219-228, 2006. Conference PapersMirko Böttcher, Martin Spott, Rudolf Kruse. Predicting future decision trees from evolving data. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), pages 33-42. IEEE Computer Society, 2008. Georg Russ, Mirko Böttcher, Detlef Nauck, and Rudolf Kruse. Relevance feedback for association rules by leveraging concepts from information retrieval. In Max Bramer, Frans Coenen, and Miltos Petridis, editors, Research and Development in Intelligent Systems XXIV, Proceedings of AI-2007, the 27th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pages 253-266. BCS SGAI, Springer London, 2007. Georg Russ, Mirko Böttcher, and Rudolf Kruse. Relevance feedback for association rules using fuzzy score aggregation. In Proceedings of the 26th International Conference of the North American Fuzzy Information Processing Society (NAFIPS'2007), pages 54-59, Piscataway, NJ, 2007. IEEE Operations Center. Frank Höppner and Mirko Böttcher. Reliably capture local clusters in noisy domains from parallel universes. In Michael R. Berthold, Katharina Morik, and Arno Siebes, editors, Parallel Universes and Local Patterns, 01.05. - 04.05.2007, number 07181 in Dagstuhl Seminar Proceedings. Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany, 2007. Frank Höppner and Mirko Böttcher. Matching partitions over time to reliably capture local clusters in noisy domains. In Joost N. Kok, Jacek Koronacki, Ramon López de Mántaras, Stan Matwin, Dunja Mladenic, and Andrzej Skowron, editors, Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings, volume 4702 of Lecture Notes in Computer Science, pages 479-486. Springer, 2007. Mirko Böttcher, Martin Spott, and Detlef Nauck. A framework for discovering and analyzing changing customer segments. In Petra Perner, editor, Advances in Data Mining. Theoretical Aspects and Applications, 7th Industrial Conference, ICDM 2007, Leipzig, Germany, July 14-18, 2007, Proceedings, volume 4597 of Lecture Notes in Computer Science, pages 255-268. Springer, 2007. Mirko Böttcher, Detlef Nauck, Dymitr Ruta, and Martin Spott. Towards a framework for change detection in datasets. In Max Bramer, Franz Coenen, and Andrew Tuson, editors, Research and Development in Intelligent Systems XXIII, Proceedings of AI-2006, the 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pages 115-128. BCS SGAI, Springer London, 2006. Detlef Nauck, Mirko Böttcher, and Martin Spott. Next generation data analysis for business applications. In Symposium on Fuzzy Systems in Computer Science (FSCS-2006), pages 74-78, 2006. Jabar Fatah, Detlef Nauck, and Mirko Böttcher. Modelling customer satisfaction using Bayesian networks. In Proceedings of the 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), pages 37-44, 2006. Mirko Böttcher, Martin Spott, and Detlef Nauck. Detecting temporally redundant association rules. In Proceedings of the Fourth International Conference on Machine Learning and Applications, pages 397-403, Washington, DC, USA, 2005. IEEE Computer Society. Book ChaptersMirko Böttcher, Georg Russ, Detlef Nauck, and Rudolf Kruse. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, Chapter From Change Mining to Relevance Feedback - A Unified View on Assessing Rule Interestingness. IGI Global, 2008. To appear May 2009. Technical ReportsMirko Böttcher, Martin Spott, and Rudolf Kruse. A temporal extension of closed item sets for change mining. Technical Report 07-08, Faculty of Computer Science, University of Magdeburg, 2008. |