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International Workshop on Knowledge Discovery in Inductive Databases

KDID 2006: Knowledge Discovery in Inductive Databases pp 259–300Cite as

Towards a General Framework for Data Mining

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4747))

Abstract

In this paper, we address the ambitious task of formulating a general framework for data mining. We discuss the requirements that such a framework should fulfill: It should elegantly handle different types of data, different data mining tasks, and different types of patterns/models. We also discuss data mining languages and what they should support: this includes the design and implementation of data mining algorithms, as well as their composition into nontrivial multi-step knowledge discovery scenarios relevant for practical application. We proceed by laying out some basic concepts, starting with (structured) data and generalizations (e.g., patterns and models) and continuing with data mining tasks and basic components of data mining algorithms (i.e., refinement operators, distances, features and kernels). We next discuss how to use these concepts to formulate constraint-based data mining tasks and design generic data mining algorithms. We finally discuss how these components would fit in the overall framework and in particular into a language for data mining and knowledge discovery.

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  1. Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia

    Sašo Džeroski

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Sašo Džeroski Jan Struyf

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Džeroski, S. (2007). Towards a General Framework for Data Mining. In: Džeroski, S., Struyf, J. (eds) Knowledge Discovery in Inductive Databases. KDID 2006. Lecture Notes in Computer Science, vol 4747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75549-4_16

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  • DOI: https://doi.org/10.1007/978-3-540-75549-4_16

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