A Nonparametric Algorithm for Detecting Clusters Using Hierarchical Structure
- 1 July 1980
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. PAMI-2 (4) , 292-300
- https://doi.org/10.1109/tpami.1980.4767028
Abstract
The present paper discusses a nonparametric algorithm for detecting clusters. In the algorithm a positive value called potential is associated with each datum based on dissimilarities. By defining subordination relations among data, hierarchical structure is introduced into the data set. As a result of the introduction of hierarchical structure, the data set is divided into some subsets called subclusters. A procedure for constructing clusters from the subclusters is also considered. The proposed algorithm can be applied to a very wide range of data set and has great ability to detect clusters, which is verified by computer simulation.Keywords
This publication has 23 references indexed in Scilit:
- A new approach to clusteringPublished by Elsevier ,2004
- A Graph-Theoretic Approach to Nonparametric Cluster AnalysisIEEE Transactions on Computers, 1976
- A locally sensitive method for cluster analysisPattern Recognition, 1976
- Clustering Using a Similarity Measure Based on Shared Near NeighborsIEEE Transactions on Computers, 1973
- A Probability Theory of Cluster AnalysisJournal of the American Statistical Association, 1973
- A Nonparametric Valley-Seeking Technique for Cluster AnalysisIEEE Transactions on Computers, 1972
- Cluster Mapping with Experimental Computer GraphicsIEEE Transactions on Computers, 1969
- Hierarchical clustering schemesPsychometrika, 1967
- Nonmetric Multidimensional Scaling: A Numerical MethodPsychometrika, 1964
- Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesisPsychometrika, 1964