# On A Maximum Likelihood Method for Clustering

On A Maximum Likelihood Method for Clustering In this paper, we propose a new clustering method based on the concept of maximum likelihood (ML) estimation. In general, the problem of local minima arises when we try to use the ML method in clustering problems. Our method circumvents this problem by employing the so called simulated annealing technique. In section 2, we formulate our clustering problem using the ML concept, and derive the ML estimation method. In section 3, validity of the derived method is confirmed by analyzing two artificial data and the famous Iris data. In the final section, our method is also extended from the viewpoint of sequential estimation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Behaviormetrika Springer Journals

# On A Maximum Likelihood Method for Clustering

, Volume 23 (2) – Jul 1, 1996
11 pages

/lp/springer-journals/on-a-maximum-likelihood-method-for-clustering-N0l9ctQ6ti
Publisher
Springer Journals
Subject
Statistics; Statistical Theory and Methods; Statistics for Business, Management, Economics, Finance, Insurance; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
ISSN
0385-7417
eISSN
1349-6964
DOI
10.2333/bhmk.23.129
Publisher site
See Article on Publisher Site

### Abstract

In this paper, we propose a new clustering method based on the concept of maximum likelihood (ML) estimation. In general, the problem of local minima arises when we try to use the ML method in clustering problems. Our method circumvents this problem by employing the so called simulated annealing technique. In section 2, we formulate our clustering problem using the ML concept, and derive the ML estimation method. In section 3, validity of the derived method is confirmed by analyzing two artificial data and the famous Iris data. In the final section, our method is also extended from the viewpoint of sequential estimation.

### Journal

BehaviormetrikaSpringer Journals

Published: Jul 1, 1996

### References

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