Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering

Dasgupta, Abhijit; Raftery, Adrian E.

doi:10.2307/2669625

Cited by 152 publications

(166 citation statements)

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“…The grid-based method (Wang et al, 1997) quantizes the object space into a finite number of cells that form a gird structure. The model-based method (Dasgupta, 1998) hypothesizes a model for each cluster and finds the best fit of the data to the given model.…”

Section: Related Workmentioning

confidence: 99%

Self-Organizing Map Learning with Momentum

Kuo

Chen

2016

CIS

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Self-organizing map (SOM) is a type of artificial neural network for cluster analysis. Each neuron in the map competes with others for the input data objects in order to learn the grouping of the input space. Besides competition, neighbor neurons of a winning neuron also learn. SOM has a natural propensity to cluster data into visually distinct clusters, which show the intrinsic grouping of data.The self-organizing map algorithm is heuristic in nature and will almost always converge. Since self-organizing map may be trapped in a local optimum, so we introduce momentum into the learning process thus the movement of a neuron may jump over local optimum. We expect this will be similar to the learning of neurons in back-propagation with momentum. Like the learning process in back-propagation, the timing for updating the amount of movement of a neuron is either batch mode or incremental mode. However, due to the neighborhood function, the movement of a non-winner neuron is relatively small as compare to when it is a winner. So when deciding the momentum, the previous movement of a neuron needs special consideration. Experiment result show that adding momentum to self-organizing map considerably contributes to the acceleration of the convergence.

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Section: Related Workmentioning

confidence: 99%

Self-Organizing Map Learning with Momentum

Kuo

Chen

2016

CIS

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“…The provision of an exhaustive list of important work over the past three decades is not feasible here. However, the following works, amongst many others, would form part of such a list: McLachlan (1982), McLachlan and Basford (1988), Banfield and Raftery (1993), Celeux and Govaert (1995), Dasgupta and Raftery (1998), McLachlan and Peel (2000b), Fraley and Raftery (2002), Raftery and Dean (2006), McLachlan et al (2007), McNicholas and Murphy (2008), and Gormley and Murphy (2008).…”

Section: Model-based Clusteringmentioning

confidence: 99%

Extending mixtures of multivariate t-factor analyzers

2010

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Model-based clustering typically involves the development of a family of mixture models and the imposition of these models upon data. The best member of the family is then chosen using some criterion and the associated parameter estimates lead to predicted group memberships, or clusterings. This paper describes the extension of the mixtures of multivariate t-factor analyzers model to include constraints on the degrees of freedom, the factor loadings, and the error variance matrices. The result is a family of six mixture models, including parsimonious models. Parameter estimates for this family of models are derived using an alternating expectation-conditional maximization algorithm and convergence is determined based on Aitken's acceleration. Model selection is carried out using the Bayesian information criterion (BIC) and the integrated completed likelihood (ICL). This novel family of mixture models is then applied to simulated and real data where clustering performance meets or exceeds that of established model-based clustering methods. The simulation studies include a comparison of the BIC and the ICL as model selection techniques for this novel family of models. Application to simulated data with larger dimensionality is also explored.

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“…After the number of clusters is set, geometrical constraints are imposed and the dimensionality of the representation is again determined by the lower value of the BIC* criterion when the values of M are increased, conditional to the previously set number of mixture components. As in Dasgupta and Raftery (1998), a rule to determine a significantly lower BIC* value is proposed; differences exceeding 0.005 min (BIC*) between two BIC* values are viewed as strong evidence that the model corresponding to the lower BIC* value is the better adjusted model.…”

Section: A Model Selection Strategymentioning

confidence: 99%

A latent class MDS model with spatial constraints for non-stationary spatial covariance estimation

Vera

Macías

Angulo

2008

Stoch Environ Res Risk Assess

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Multidimensional scaling (MDS) has played an important role in non-stationary spatial covariance structure estimation and in analyzing the spatiotemporal processes underlying environmental studies. A combined cluster-MDS model, including geographical spatial constraints, has been previously proposed by the authors to address the estimation problem in oversampled domains in a least squares framework. In this paper is formulated a general latent class model with spatial constraints that, in a maximum likelihood framework, allows to partition the sample stations into classes and simultaneously to represent the cluster centers in a low-dimensional space, while the stations and clusters retain their spatial relationships. A model selection strategy is proposed to determine the number of latent classes and the dimensionality of the problem. Real and artificial data sets are analyzed to test the performance of the model.

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Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering

Cited by 152 publications

References 0 publications

Self-Organizing Map Learning with Momentum

Self-Organizing Map Learning with Momentum

Extending mixtures of multivariate t-factor analyzers

A latent class MDS model with spatial constraints for non-stationary spatial covariance estimation

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