Model-based clustering of high-dimensional data: A review

Bouveyron, Charles; Brunet-Saumard, Camille

doi:10.1016/j.csda.2012.12.008

Cited by 385 publications

(252 citation statements)

References 95 publications

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“…This is achieved by an iterative process for determining optimal clustering centers and in this iterative process similarities are calculated based on the square error criterion [8]. Clustering methods have * Correspondence: omerfaruk.ertugrul@batman.edu.tr been employed in many research problems such as biomedical datasets [11], high-dimensional problems [12], and times signals [13]. In addition to clustering, these methods were also employed to categorize samples in order to reduce the length of the dataset and determine ideal exemplars as a category definition [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

Ertuğrul¹

2017

Turk J Elec Eng & Comp Sci

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Abstract:The number and length of massive datasets have increased day by day and this yields more complex machine learning stages due to the high computational costs. To decrease the computational cost many methods were proposed in the literature such as data condensing, feature selection, and filtering. Although clustering methods are generally employed to divide samples into groups, another way of data condensing is by determining ideal exemplars (or prototypes), which can be used instead of the whole dataset. In this study, first the efficiency of traditional data condensing by clustering approach was confirmed according to obtained accuracies and condensing ratios in 9 different synthetic or real batch datasets. This approach was then improved to be employed in time-ordered datasets. In order to validate the proposed approach, 23 different real time-ordered datasets were used in experiments. Achieved mean RMSEs were 0.27 and 0.29 by employing the condensed (mean condensed ratio was 97.17%) and the whole datasets, respectively. Obtained results showed that higher accuracy rates and condensing ratios were achieved by the proposed approach.

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Section: Introductionmentioning

confidence: 99%

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

Ertuğrul¹

2017

Turk J Elec Eng & Comp Sci

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“…The analysis of high dimensional data implies to bear in mind data whose dimension is larger than dimensions considered in classical multivariate analysis. As indicated by Bouveyron et al [43], when conventional methods deal with high dimensional data they are susceptible to suffer the well-known curse of dimensionality, where considering a large number of irrelevant, redundant and noisy attributes leads to important prediction errors. Hence operate with this data implies the need for more specific and complex algorithms.…”

Section: Design Principlesmentioning

confidence: 99%

An Approach to Data Analysis in 5G Networks

López

Vidal

Villalba

2017

Entropy

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5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET) Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.

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“…One may speed up the EM-BIC scheme by placing reasonable bounds on M or by using other efficient fitting schemes (e.g., Sondergaard and Lermusiaux 2013a;Bouveyron and Brunet-Saumard 2014). Convergence can also be accelerated by choosing a suitable initial guess for the unknown mixture components in the EM algorithm.…”

Section: ) Computational and Storage Costsmentioning

confidence: 99%

A Gaussian Mixture Model Smoother for Continuous Nonlinear Stochastic Dynamical Systems: Theory and Scheme

Lolla

Lermusiaux

2017

Mon. Wea. Rev.

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Retrospective inference through Bayesian smoothing is indispensable in geophysics, with crucial applications in ocean and numerical weather estimation, climate dynamics, and Earth system modeling. However, dealing with the high-dimensionality and nonlinearity of geophysical processes remains a major challenge in the development of Bayesian smoothers. Addressing this issue, a novel subspace smoothing methodology for high-dimensional stochastic fields governed by general nonlinear dynamics is obtained. Building on recent Bayesian filters and classic Kalman smoothers, the fundamental equations and forwardbackward algorithms of new Gaussian Mixture Model (GMM) smoothers are derived, for both the full state space and dynamic subspace. For the latter, the stochastic Dynamically Orthogonal (DO) field equations and their time-evolving stochastic subspace are employed to predict the prior subspace probabilities. Bayesian inference, both forward and backward in time, is then analytically carried out in the dominant stochastic subspace, after fitting semiparametric GMMs to joint subspace realizations. The theoretical properties, varied forms, and computational costs of the new GMM smoother equations are presented and discussed.

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Model-based clustering of high-dimensional data: A review

Cited by 385 publications

References 95 publications

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

An Approach to Data Analysis in 5G Networks

A Gaussian Mixture Model Smoother for Continuous Nonlinear Stochastic Dynamical Systems: Theory and Scheme

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