Spatial stochastic process clustering using a local a posteriori probability

Grall‐Maës, Edith

doi:10.1109/mlsp.2014.6958850

Cited by 2 publications

(1 citation statement)

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“…For a given number of clusters, an algorithm for determining the parameters of the probability density functions and the proportions has been introduced in (Shental et al, 2003). A modified version has been proposed in (Grall-Maës, 2014) when partitioning the data is the main concern. However as in classical clustering problems, the number of clusters is generally unknown.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Number of Clusters in a Mixture Model with Side-information

Grall‐Maës

Dao

2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

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This paper deals with the selection of cluster number in a clustering problem taking into account the sideinformation that some points of a chunklet arise from a same cluster. An Expectation-Maximization algorithm is used to estimate the parameters of a mixture model and determine the data partition. To select the number of clusters, usual criteria are not suitable because they do not consider the side-information in the data. Thus we propose suitable criteria which are modified version of three usual criteria, the bayesian information criterion (BIC), the Akaike information criterion (AIC), and the entropy criterion (NEC). The proposed criteria are used to select the number of clusters in the case of two simulated problems and one real problem. Their performances are compared and the influence of the chunklet size is discussed.

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Section: Introductionmentioning

confidence: 99%