Check our tutorials on state-of-the-art clustering methods!
Acronym | Data Type | Software | Note | |
CPF | Multivariate | Slides Tutorial |
PyPI | CPF (Component-wise Peak-Finding) is an improvement over DCF: (1) it applies the density peaks methodology within level sets of the estimated density; (2) the algorithm is not affected by spurious maxima of the density. |
DCF | Multivariate | Slides Tutorial |
GitHub | DCF (Density Core Finding) is able to detect clusters of varying density and irregular shape, and
applicable to big data with numerous clusters. The idea is to detect high-density core regions, each region representing a cluster, and then assign each non-core point to the same cluster as its nearest neighbor of higher density. |
FAE | Functional | Slides Tutorial |
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GPmix | Functional | Slides Tutorial |
GitHub | GPmix (Gaussian Process mixture) is for learning mixtures of Gaussian processes. It is built on the property that the projection coefficients of the functional data onto any given projection function follow a univariate Gaussian mixture model. |
REM | Multivariate | Slides Tutorial |
PyPI | REM (Reinforced EM) provides an efficient solution to the initilization problem of the EM algorithm for clustering with Gaussian mixture models. It initializes the Gaussian means with density-peak exemplars in the data. |