Task and method selection: selection of data mining methods for tasks C5.1--C5.8

Authors:
Guido Lindner;Robert Engels;Rudi Studer
Affiliations:
Württembergische Versicherung AG, Stuttgart, Germany;Manager, Research and Development Projects, CognIT a.s., Halden, Norway;Professor of Applied Informatics, Institute for Applied Informatics and Formal Description Methods (AIFB), University of Karlsruhe, Germany
Venue:
Handbook of data mining and knowledge discovery
Year:
2002

Citing 7
Cited 0

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Characterizing the applicability of classification algorithms using meta-level learning

ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Lazy learning

Lazy learning
Case-Based Reasoning: Experiences, Lessons and Future Directions

Case-Based Reasoning: Experiences, Lessons and Future Directions
The Mathematics of Generalization: The Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning

The Mathematics of Generalization: The Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning
Induction of Decision Trees

Machine Learning
AST: Support for Algorithm Selection with a CBR Approach

PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

Once a data mining task is determined, as described in Chapter 17.1, appropriate methods have to be selected for execution of this task. In this article, issues are discussed that play a part in choosing among alternative models for performing such data mining tasks. We argue that method selection depends highly on the application context as given by initial task analysis, on the properties of the data the analysis is performed on, on previous experiences with similar domains, and on user-specified requirements on the results.