W. Martinez and A. Martinez (2005). Exploratory data analysis with MATLAB. Chapman &amp; Hall/CRC Press. 405+xv pages. US$79.95. ISBN: 1-58488-366-9.

Davis‐Stober, Clintin P.; Broomell, Stephen B.; Lorenz, Florian

doi:10.1007/s11336-005-1362-2

Cited by 5 publications

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“…The EM procedure consists of the Expectation and Maximization steps. While the Expectation step is relatively simple, the Maximization usually needs complicated numerical optimization even for relatively simple Gaussian models [38,39,40]. In this paper we propose the afCEC method which is based on the CEC model, instead of the Expectation Maximization (EM) and Gaussian density model in a curvilinear coordinate system.…”

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confidence: 99%

Active function Cross-Entropy Clustering

Spurek

Tabor

Byrski

2017

Expert Systems with Applications

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Gaussian Mixture Models (GMM) have found many applications in density estimation and data clustering. However, the model does not adapt well to curved and strongly nonlinear data. Recently there appeared an improvement called AcaGMM (Active curve axis Gaussian Mixture Model), which fits Gaussians along curves using an EM-like (Expectation Maximization) approach.Using the ideas standing behind AcaGMM, we build an alternative active function model of clustering, which has some advantages over AcaGMM. In particular it is naturally defined in arbitrary dimensions and enables an easy adaptation to clustering of complicated datasets along the predefined family of functions. Moreover, it does not need external methods to determine the number of clusters as it automatically reduces the number of groups on-line.

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confidence: 99%