DECA: A Discrete-Valued Data Clustering Algorithm

@article{Wong1979DECAAD,
  title={DECA: A Discrete-Valued Data Clustering Algorithm},
  author={Andrew K. C. Wong and David C. C. Wang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={1979},
  volume={PAMI-1},
  pages={342-349}
}
This paper presents a new clustering algorithm for analyzing unordered discrete-valued data. This algorithm consists of a cluster initiation phase and a sample regrouping phase. The first phase is based on a data-directed valley detection process utilizing the optimal second-order product approximation of high-order discrete probability distribution, together with a distance measure for discrete-valued data. As for the second phase, it involves the iterative application of the Bayes' decision… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 13 references

Pattern classification and scene analysis

A Wiley-Interscience publication • 1973
View 4 Excerpts
Highly Influenced

Numerical Taxonomy

N. Jardine, R. Sibson
New York: Wiley • 1971
View 1 Excerpt

Mapping diversity: A comparative study of some numerical methods," in Numerical Taxonomy

A. J. Boyce
Proceedings of the Colloquium in Numerical Taxonomy Held in the University of St. Andrews, • 1968
View 1 Excerpt

Clustering analysis

K. S. Fu
Digital Pattern Recognition • 1965

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