Book ChapterDOI
A Recent Advance in Data Analysis: Clustering Objects into Classes Characterized by Conjunctive Concepts
Ryszard S. Michalski,Robert E. Stepp,Edwin Diday +2 more
- pp 33-56
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TLDR
The paper explains the recently introduced method of conjunctive conceptual clustering in terms of dynamic clustering and shows by an example its advantages over methods of numerical taxonomy from the viewpoint of cluster interpretation.Abstract:
Clustering is described as a multistep process in which some of the steps are performed by a data analyst and some by a computer program. At present, those performed by a computer program do not produce any description of the generated clusters. The recently introduced method of conjunctive conceptual clustering overcomes this problem by requiring that each cluster has a conjunctive description built from relations on object attributes and closely “fitting” the cluster. The paper explains the above clustering method in terms of dynamic clustering and shows by an example its advantages over methods of numerical taxonomy from the viewpoint of cluster interpretation.read more
Citations
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Data clustering: a review
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The role of theories in conceptual coherence.
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Improved heterogeneous distance functions
D. Randall Wilson,Tony Martinez +1 more
TL;DR: This article proposed three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference metric (IVDM), and the Windowed Value Difference measure (WVDM) to handle applications with nominal attributes, continuous attributes and both.
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Reduction Techniques for Instance-BasedLearning Algorithms
D. Randall Wilson,Tony Martinez +1 more
TL;DR: Of those algorithms that provide substantial storage reduction, the DROP algorithms have the highest average generalization accuracy in these experiments, especially in the presence of uniform class noise.
References
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Journal ArticleDOI
A clustering technique for summarizing multivariate data.
Geoffrey H. Ball,David J. Hall +1 more
TL;DR: A practical computing method termed ISODATA, which finds the cluster structure of such data, is described and provides a fit to the data of a set of cluster centers that tends to minimize the sum of the squared distances of each data point from its closest cluster center.
Book ChapterDOI
Variable-Valued Logic and Its Applications to Pattern Recognition and Machine Learning
TL;DR: This chapter discusses the applications of variable-valued logic to pattern recognition and machine learning and presents some results on the application of an extended form of multi-valued Logic called variable- valued logic topattern recognition and artificial intelligence.
Book ChapterDOI
Clustering in Pattern Recognition
TL;DR: The main basic choices which are preliminary to any clustering are presented and the dynamic clustering method which gives a solution to a family of optimization problems related to those choices is presented.
Journal ArticleDOI
Spatial clustering procedures for region analysis
TL;DR: Two clustering procedures for region analysis of image data are described and the security of these algorithms theoretically and examples are presented in order to show how these algorithms work for real image data.