Results 281 to 290 of about 1,500,611 (328)

Model-based overlapping clustering

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005
While the vast majority of clustering algorithms are partitional, many real world datasets have inherently overlapping clusters. Several approaches to finding overlapping clusters have come from work on analysis of biological datasets. In this paper, we interpret an overlapping clustering model proposed by Segal et al.
Arindam Banerjee 0001, Chase Krumpelman, Joydeep Ghosh, Sugato Basu, Raymond J. Mooney   +4 more
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Model-Based Clustering with HDBSCAN*

2021
We propose an efficient model-based clustering approach for creating Gaussian Mixture Models from finite datasets. Models are extracted from HDBSCAN* hierarchies using the Classification Likelihood and the Expectation Maximization algorithm. Prior knowledge of the number of components of the model, corresponding to the number of clusters, is not ...
Michael Strobl, Jörg Sander 0001, Ricardo J. G. B. Campello, Osmar R. Zaïane   +3 more
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Model‐based linear clustering

Canadian Journal of Statistics, 2010
AbstractThe authors propose a profile likelihood approach to linear clustering which explores potential linear clusters in a data set. For each linear cluster, an errors‐in‐variables model is assumed. The optimization of the derived profile likelihood can be achieved by an EM algorithm.
Yan, Guohua, Welch, William J., Zamar, Ruben H.   +2 more
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Model-Based Edge Clustering

Journal of Computational and Graphical Statistics, 2020
Relational data can be studied using network analytic techniques which define the network as a set of actors and a set of edges connecting these actors.
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Model-based cluster analysis

Pattern Recognition, 1993
Abstract The problem of dot clustering is studied from a model-based viewpoint. A set of “placement” processes is chosen, each of which associates a probability with each location in a discrete space; in other words, a placement is a probability mass function (pmf) on the space.
Saibal Banerjee, Azriel Rosenfeld
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Model-based Clustering of Count Processes

Journal of Classification, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tin Lok James Ng, Thomas Brendan Murphy
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A model-based distance for clustering

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000
A Riemannian distance is defined which is appropriate for clustering multivariate data. This distance requires that data is first fitted with a differentiable density model allowing the definition of an appropriate Riemannian metric. A tractable approximation is developed for the case of a Gaussian mixture model and the distance is tested on artificial
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Challenges in model‐based clustering

WIREs Computational Statistics, 2013
AbstractModel‐based clustering is an increasingly popular area of cluster analysis that relies on probabilistic description of data by means of finite mixture models. Mixture distributions prove to be a powerful technique for modeling heterogeneity in data.
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Model-Based Clustering

2009
Finite mixture models are being commonly used in a wide range of applications in practice concerning density estimation and clustering. An attractive feature of this approach to clustering is that it provides a sound statistical framework in which to assess the important question of how many clusters are there in the data and their validity.
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Model-based clustering for longitudinal data

Computational Statistics & Data Analysis, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rolando De la Cruz-Mesía, Fernando A. Quintana, Guillermo Marshall   +2 more
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