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
Geodetic datumNonparametric statisticsComputer scienceSet (abstract data type)AlgorithmData setData structureRange (aeronautics)Data miningHierarchical database modelPattern recognition (psychology)Artificial intelligenceMathematicsStatistics
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Publication Info
- Year
- 1980
- Type
- article
- Volume
- PAMI-2
- Issue
- 4
- Pages
- 292-300
- Citations
- 22
- Access
- Closed
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Cite This
Riichiro Mizoguchi,
Masamichi Shimura
(1980).
A Nonparametric Algorithm for Detecting Clusters Using Hierarchical Structure.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, PAMI-2
(4)
, 292-300.
https://doi.org/10.1109/tpami.1980.4767028
Identifiers
- DOI
- 10.1109/tpami.1980.4767028