Parallel Computing for Dimensionality Reduction

Zubova, Jelena; Liutvinavicius, Marius; Kurasova, Olga

doi:10.1007/978-3-319-46254-7_19

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Big data analytics is the process of investigating big data to uncover hidden and useful information for better decisions. It involves visual presentation of data that enables to see hidden relations between objects which cannot be detected using conventional data analysis methods [14].…”

Section: Introductionmentioning

confidence: 99%

Dimensionality reduction methods: the comparison of speed and accuracy

Zubova¹,

Kurasova²,

Liutvinavicius³

2018

ITC

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This research focuses on big data visualization that is based on dimensionality reduction methods. We propose a multi-level method for data clustering and visualization. It divides the whole data mining process into separate steps and applies particular dimensionality reduction method considering to analyzed data volume and type. The methods are selected according to their speed and accuracy. Therefore, we present a comparison of the selected methods according to these two criteria. Three groups of datasets containing different kind of data are used for methods evaluation. The factors that influence speed or accuracy are determined. The rank of investigated methods based on research results is presented in this paper.

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Section: Introductionmentioning

confidence: 99%

Dimensionality reduction methods: the comparison of speed and accuracy

Zubova¹,

Kurasova²,

Liutvinavicius³

2018

ITC

Self Cite

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“…Big data analytics is the process of investigating big data to uncover hidden and useful information for better decisions. It involves a visual presentation of data that enables to see hidden relations between objects, which cannot be detected using conventional data analysis methods (Zubova et al, 2016). Recently this topic has been widely investigated by various researchers (Yongjie et al, 2018), (Khomtchouk et al, 2017), (Xuedi et al, 2018), (Domeniconi, 2004), (Diamond and Mattia, 2017).…”

Section: Introductionmentioning

confidence: 99%

Multi-level Massive Data Visualization: Methodology and Use Cases

Liutvinaviciene¹,

Kurasova²

2018

BJMC

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This research focuses on massive data visualization that is based on dimensionality reduction methods. We propose a new methodology, which divides the whole data visualization process into separate interactive steps. In each step, some part of data can be selected for further analysis and visualization. The different dimensionality method can be chosen/changed in each step. The decision which methods to be chosen depends on desirable accuracy measures and visualization samples. In addition, there are provided statistical measures of the identified clusters. We have developed a special tool, which implements the proposed methodology. R language and Shiny package were used for developing the tool. In the paper, the principles of the methodology and features of the tool are presented by describing the specific use case.

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A Review of Dimensionality Reduction in High-Dimensional Data Using Multi-core and Many-core Architecture

Patil¹,

Kulkarni²

2019

Communications in Computer and Information Science

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Parallel Computing for Dimensionality Reduction

Cited by 3 publications

References 8 publications

Dimensionality reduction methods: the comparison of speed and accuracy

Dimensionality reduction methods: the comparison of speed and accuracy

Multi-level Massive Data Visualization: Methodology and Use Cases

A Review of Dimensionality Reduction in High-Dimensional Data Using Multi-core and Many-core Architecture

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