Abstract
Most data mining systems follow a data flow and toolbox paradigm. While this modular approach delivers ultimate flexibility, it gives the user almost no guidance on the issue of choosing an efficient combination of algorithms in the current problem context. In the field of Software Engineering the Pattern Based development process has empirically proven its high potential. Patterns provide a broad and generic framework for the solution process in its entirety and are based on equally broad characteristics of the problem. Details of the individual steps are filled in at later stages. Basic research on pattern based thinking has provided us with a list of generally applicable and proven patterns. User interaction in a pattern based approach to data mining will be divided into two steps: (1) choosing a pattern from a generic list based an a handful of characteristics of the problem and later (2) filling in data mining algorithms for the subtasks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
ALEXANDER, C. (1979): The Timeless Way of Building, Oxford University Press.
ALEXANDER, C. (2002a): The Nature of Order Book 1: The Phenomenon of Life, The Center for Environmental Structure, Berkeley, California.
ALEXANDER, C. (2002b): The Nature of Order Book 2: The Process of Creating Life, The Center for Environmental Structure, Berkeley, California.
CHAPMAN, P., CLINTON, J., KERBER, R., KHABAZA, T., REINARTZ, T., SHEARER, C. and WIRTH, R. (2000): CRISP-DM 1.0. Step-by-step data mining guide, www.crisp-dm.org.
COPLIEN, J.O.(1996): Software Patterns, SIGS Books & Multimedia.
COPLIEN, J.O. and ZHAO, L. (2005): Toward a General Formal Foundation of Design -Symmetry and Broken Symmetry, Brussels: VUB Press.
ECKERT, C. and CLARKSON, J. (2005): Design Process Improvement: a review of current practice, Springer Verlag London.
FAYYAD, U.M., PIATETSKY-SHAPIRO, G. and UTHURUSAMY, R. (Ed.) (1996): Ad-vances in Knowledge Discovery and Data Mining, MIT Press.
GAMMA, E., HELM, R., JOHNSON, R. and VLISSIDES, J. (1995): Design Patterns. Ele-ments of Reusable Object-Oriented Software, Addison-Wesley.
HIPPNER, H., MERZENICH, M. and STOLZ, C. (2002): Data Mining: Einsatzpotentiale und Anwendungspraxis in deutschen Unternehmen, In: WILDE, K.D.: Data Mining Studie, absatzwirtschaft.
RAKOTOMALALA, R. (2004): Tanagra - A free data mining software for research and edu-cation, www.eric.univ-lyon2.fr/∼rico/tanagra/.
WITTEN, I.H. and FRANK, E. (2005): Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann, San Francisco.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Delibašić, B., Kirchner, K., Ruhland, J. (2008). A Pattern Based Data Mining Approach. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_39
Download citation
DOI: https://doi.org/10.1007/978-3-540-78246-9_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78239-1
Online ISBN: 978-3-540-78246-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)