Learning in intractable domains
Machine learning: a guide to current research
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
On changing continuous attributes into ordered discrete attributes
EWSL-91 Proceedings of the European working session on learning on Machine learning
Employing linear regression in regression tree leaves
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
Applied Intelligence
Feature Transformation by Function Decomposition
IEEE Intelligent Systems
Machine Learning
Constructing Intermediate Concepts by Decomposition of Real Functions
ECML '97 Proceedings of the 9th European Conference on Machine Learning
A Practical Approach to Feature Selection
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Functional Models for Regression Tree Leaves
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Discretization and Grouping: Preprocessing Steps for Data Mining
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
An efficient preprocessing stage for the relationship-based clustering framework
Intelligent Data Analysis
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Knowledge Discovery in Databases (KDD) has become a very attractive discipline both for research and industry within last few years. Its goal is to extract pieces of knowledge or `patterns' from usually very large databases. It portrays a robust sequence of procedures or steps that have to be carried out to derive reasonable and understandable results. One of its components symbolizes an inductive process that induces the above pieces of knowledge; usually it is Machine Learning (ML). However, most of the machine learning algorithms require perfect data in a reasonable format. Therefore, some preprocessing routines as well as postprocessing ones should fill the entire chain of data processing. This paper overviews and discusses the knowledge discovery process and its methodology as a series of several steps which include machine learning, preprocessing of data, and postprocessing of the results induced.