Clustering via the Distribution Mixtures

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F10%3A00176166" target="_blank" >RIV/68407700:21340/10:00176166 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Clustering via the Distribution Mixtures
Original language description
The finite distribution mixtures present a wide class of probability distributions. Apart from the obvious applications, the mixtures are successfully applied in the model based clustering. If we constraint the members of the mixture to arise from one specific family or type of parametric distributions, each cluster would refer to one component of the mixture. The membership of the observed sample to a cluster is given simply as the maximum probability on the components of the mixture, i.e. by the Mahalanobis distance, and weighted by the weights of the mixture. This approach is feasible even for overlapping clusters and strongly uneven numbers of the members of the clusters, where standard methods of cluster analysis fall short. We focus on the problem of fitting the mixture to observed sample using the maximum likelihood approach and the EM algorithm, as well as the assessment of the optimal number of components.
Czech name
—
Czech description
—

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)