Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters

Tellaroli, Paola; Bazzi, Marco; Donato, Michèle; Brazzale, Alessandra Rosalba; Drăghici, Sorin

doi:10.1371/journal.pone.0152333

Cited by 29 publications

(18 citation statements)

References 48 publications

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“…In contrast to many other clustering methods, in hierarchical clustering the number of clusters is not chosen before the clustering is performed, but often from an "intuitive" inspection of a dendrogram (see below for further details) [Tellaroli et al, 2016]. Here the appropriate number of clusters (k) to extract was informed from the "gap statistic" [Tibshirani et al, 2001].…”

Section: Heat Wave Evaluation Proceduresmentioning

confidence: 99%

Comparing Australian heat waves in the CMIP5 models through cluster analysis

et al. 2017

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Quantitative projections of climate extremes on local to regional scales are highly valuable for planners and decision makers and necessary for effective local climate change adaptation. However, in contrast to the model robustness of simulated extremes at the global scale, the robustness in simulating past and future extremes often diminishes over finer spatial scales. In this study we analyze heat waves simulated by state‐of‐the‐art global climate models over the Australian region. For the first time we present results explicitly detailing the model spread in simulated heat wave trends and climatology for this region for the recent past (1958–2005). As expected, large intermodel spread is observed at the local to regional scale for both heat wave trends and climatology. By analyzing multiple initial condition runs from individual models, we show that model internal variability strongly influences the spatial patterns of heat wave trends, while intermodel differences in heat wave climatology appear more influenced by model uncertainty. From a model evaluation perspective, cluster analysis is shown to be useful in characterizing robust spatial features of heat waves simulated by the models. In contrast to the multimodel mean, where uncorrelated spatial features tend to be averaged out, cluster composites preserve these features. Since previous examinations have tended to focus on the multimodel mean the extent of model spread may have been overlooked. Further examination of the processes that lead to model differences and biases is needed.

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Section: Heat Wave Evaluation Proceduresmentioning

confidence: 99%

Comparing Australian heat waves in the CMIP5 models through cluster analysis

et al. 2017

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“…These authors tried to find sunflower adulteration of EVOO. For this they examined the wavelengths of Finally, we should refer a new method of hierarchical clustering that combines the advantages of the strategies (1) Ward's minimum variance and (2) centroid clustering (Tellaroli, Bazzi, Donato, Brazzale, & Drăghici, 2016). The method was named by the authors "Cross Clustering (CC)" was developed in order to avoid four usual pitfalls, namely, 1. the lack of a strategy to deal with outliers; 2. the lack of an estimate of the number of clusters;…”

Section: Hierarchical Clustering (Hca) Methodsmentioning

confidence: 99%

“…Finally, we should refer a new method of hierarchical clustering that combines the advantages of the strategies (1) Ward's minimum variance and (2) centroid clustering (Tellaroli, Bazzi, Donato, Brazzale, & Drăghici, ). The method was named by the authors “Cross Clustering (CC)” was developed in order to avoid four usual pitfalls, namely, the lack of a strategy to deal with outliers; the lack of an estimate of the number of clusters; the lack of the inappropriateness of a specific partitioning; and the lack of independence of the resulting classification to the initial configuration. …”

Section: Multivariate Proceduresmentioning

confidence: 99%

Chemometrical and molecular methods in olive oil analysis: A review

Avramidou¹,

Doulis²,

Petrakis³

2018

J Food Process Preserv

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Foods based on Olea europaea are of substantial importance for Mediterranean people. The association between olive products with great civilizations emphasizes the role that played the olive tree products and especially the olive oil. This generated the need for authentication, quality control and traceability of olive products. The physicochemical and sensory analysis methods, both classical (discrimination, ordination, classification) and novel (artificial neural networks, fuzzy logic, expert systems, decision trees, support vector machines) supplemented with molecular marker techniques (SSR, AFLP, RAPD), generated the need for effective multivariate analytical methods. These mathematically complex methods are greatly needed in the detection of adulterations of oil products and particularly for olive oil. The lack of mathematical symbolism and jargon is believed to make the text attractive to all potential workers in the field and help them improve the already low level of adulterations and augment the quality control databases for Mediterranean olive products. Practical applications In this review, multivariate methods applied until now in chemometrics supplemented with molecular methods of olive‐based products were presented. Data processing in chemometris and molecular methods is an important issue in order to examine, explain, and evaluate such priorities of olive oil‐based products. This review will facilitate researches to select the best suitable statistic model or equation regarding their hypothesis and use the most appropriate method in order to improve the low level of adulterations and augment in the quality control databases for Mediterranean olive products.

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“…The condensed hierarchical clustering algorithm is a bottom-up approach [31,32]. The word "condensed" means that the algorithm takes each sample as a single cluster at the initial stage.…”

Section: Condensed Hierarchical Clustering Algorithmmentioning

confidence: 99%

A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Yang

Yao

et al. 2019

Energies

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Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.

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Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters

Cited by 29 publications

References 48 publications

Comparing Australian heat waves in the CMIP5 models through cluster analysis

Comparing Australian heat waves in the CMIP5 models through cluster analysis

Chemometrical and molecular methods in olive oil analysis: A review

A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

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