Cluster Detection in Noisy Environment by Using the Modified EM Algorithm
| Autor: | Zlatko Pavić, Vedran Novoselac |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Economics and Econometrics Mahalanobis distance Current (mathematics) Computer science Applied Mathematics lcsh:T57-57.97 Construct (python library) Management Science and Operations Research clustering EM least squares least absolute devoation Davies-Bouldin index Hidden variable theory Expectation–maximization algorithm lcsh:Applied mathematics. Quantitative methods Cluster (physics) least absolute deviation Statistics Probability and Uncertainty Algorithm |
| Zdroj: | Croatian Operational Research Review Volume 9 Issue 2 Croatian Operational Research Review, Vol 9, Iss 2, Pp 223-234 (2018) |
| ISSN: | 1848-0225 1848-9931 |
| Popis: | The paper studies the problem of a cluster detection in the noisy environment. The solution of this problem is based on the well known Expectation Maximization (EM) algorithm. By utilizing the Mahalanobis distance, and modifying the hidden variable, the rejection procedure is constructed so that it omits data from calculation of the current iteration step. Thus we construct the adaptive framework for solving the above problem. Several numerical examples are presented to illustrate the proposed algorithm. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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