Strategies for Big Data Clustering

Kurasova, Olga; Marcinkevičius, Virginijus; Medvedev, Viktor; Rapečka, Aurimas; Stefanovič, Pavel

doi:10.1109/ictai.2014.115

Cited by 46 publications

(16 citation statements)

References 27 publications

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“…Regions were created by non-hierarchical k-means cluster analysis, because statistical literature advises its use for larger datasets and it is simple to apply (Kurasova et al, 2014). The input dataset was divided into k-clusters so that intra-cluster similarity was as high as possible and inter-cluster similarity as low as possible.…”

Section: Cluster Analysismentioning

confidence: 99%

Vineyard zonation based on natural terroir factors using multivariate statistics – Case study Burgenland (Austria)

et al. 2018

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Aim: The aim of the study was to explicitly develop a methodology of delineation of natural Protected Designation of Origins (PDO) terroir regions for Austria, where PDO viticulture regions reflect natural conditions only in a formal manner.Methods and results: There is increasing competition in the wine market from globalized trends, where the European Union (EU) and non-EU wine producers have adopted different market strategies to promote their wines and gain larger market share. The EU has therefore established protective agricultural product categories such as PDO and Protected Geographical Indication (PGI) based on the terroir concept which contrasts with USA strategies. Here, we first collected and derived as many as possible relevant physical geographical data for a total of 66,673 officially registered vineyard areas in Burgenland (Austria). Next, we applied factor analysis to these data with the aim to shrink their size and reduce their dimensionality. For each vineyard plot was derived a factor score which was used for performing k-means clustering. The best count of clusters, k-parameter, was estimated using five internal validity indices. Five homogenous management zones were created as a result of clustering. Correctness and accuracy of the clustering was evaluated by multidimensional discriminant analysis. The final zones were compared to current Districtus Austriae Controllatus (DAC) of Burgenland.Conclusion: It was found by the comparison of DAC regions of Burgenland and our drafted zones that some of the DAC regions do not respect natural terroir zones, while these regions were created as PDO regions which should respect their natural terroir.Significance and impact of the study: The presented methodology can be applied all over Austria and, with some modifications caused by different input data, to each EU member country where it is necessary to revise PDO regions’ borders.

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Section: Cluster Analysismentioning

confidence: 99%

Vineyard zonation based on natural terroir factors using multivariate statistics – Case study Burgenland (Austria)

et al. 2018

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“…Olga Kurasova et al [15] overviewed the various methods and technologies which can be used for big data clustering. They paid the great attention to the K-Means clustering and its modifications and are implemented in innovative technologies for big data analysis.…”

Section: Grid-based Clustering:-mentioning

confidence: 99%

Performance Evaluation of Weka Clustering Algorithms on Large Datasets.

Parmar¹,

Chauhan²,

Bansal.³

2017

IJAR

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“…Yao et al (2013) used k-Means clustering algorithm to select the most informative samples into small subset from original training set for SVM training. Kurasova et al (2014) presented an overview of techniques used for big data clustering and also identified k-means as one of the most popu-lar and efficient techniques. Gan et al (2017) used k-Means to construct a pre-selection scheme, which obtains a subset of important instances as training set for SVM.…”

Section: Introductionmentioning

confidence: 99%

SVM and k-Means Hybrid Method for Textual Data Sentiment Analysis

Korovkinas¹,

Danėnas²,

Garšva³

2019

BJMC

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The goal of this paper is to propose a hybrid technique to improve Support Vector Machines classification accuracy using training data sampling and hyperparameter tuning. The proposed technique applies clustering to select training data and parameter tuning to optimize classifier effectiveness. The paper reports that better results were obtained using our proposed method in all experiments, compared to results of method presented in our previous work.

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Strategies for Big Data Clustering

Cited by 46 publications

References 27 publications

Vineyard zonation based on natural terroir factors using multivariate statistics – Case study Burgenland (Austria)

Vineyard zonation based on natural terroir factors using multivariate statistics – Case study Burgenland (Austria)

Performance Evaluation of Weka Clustering Algorithms on Large Datasets.

SVM and k-Means Hybrid Method for Textual Data Sentiment Analysis

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