Protos: an examplar-based learning apprentice
International Journal of Man-Machine Studies
Knowledge discovery in databases: an overview
AI Magazine
ACM Computing Surveys (CSUR)
The analysis of a simple k-means clustering algorithm
Proceedings of the sixteenth annual symposium on Computational geometry
Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
Explanation-Based Generalization: A Unifying View
Machine Learning
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Similarity Measures for Object-Oriented Case Representations
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
The Explanatory Power of Symbolic Similarity in Case-Based Reasoning
Artificial Intelligence Review
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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The goal of Knowledge Discovery is to extract knowledge from a set of data. Most common techniques used in knowledge discovery are clustering methods, whose goal is to analyze a set of objects and obtain clusters based on the similarity among these objects. A desirable characteristic of clustering results is that these should be easily understandable by domain experts. In fact, these are characteristics that exhibit the results of eager learning methods (such as ID3) and lazy learning methods when used for building lazy domain theories. In this paper we propose LazyCL, a procedure using a lazy learning method to produce explanations on clusters of unlabeled cases. The analysis of the relations among these explanations converges to a correct clustering of the data set.