<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the arrival of the significant data era, efficiently processing large-scale multidimensional data has become challenging. As a powerful data dimensionality reduction tool, Principal Component Analysis (PCA) plays a vital role in big data processing, especially in information extraction and data simplification, showing unique advantages. The research aims to simplify the data processing process and improve the data processing efficiency by PCA method. The research method adopts the basic theory of PCA, the improvement of the weighted principal component analysis algorithm, and standardized and homogenized data processing techniques to process large-scale multidimensional data sets. The results show that the data dimensionality is significantly reduced after using PCA, for example, in the Analysis of the earnings quality of listed companies in the e-commerce industry, the cumulative variance contribution rate of the first four principal components extracted by PCA reaches 81.623%, which effectively removes the primary information of the original data. PCA not only reduces the complexity of the data, but also retains a large amount of crucial information, which is a significant application value for the processing of big data, especially in the fields of data compression and pattern recognition.</p>
</abstract>

With the arrival of the significant data era, efficiently processing large-scale multidimensional data has become challenging. As a powerful data dimensionality reduction tool, Principal Component Analysis (PCA) plays a vital role in big data processing, especially in information extraction and data simplification, showing unique advantages. The research aims to simplify the data processing process and improve the data processing efficiency by PCA method. The research method adopts the basic theory of PCA, the improvement of the weighted principal component analysis algorithm, and standardized and homogenized data processing techniques to process large-scale multidimensional data sets. The results show that the data dimensionality is significantly reduced after using PCA, for example, in the Analysis of the earnings quality of listed companies in the e-commerce industry, the cumulative variance contribution rate of the first four principal components extracted by PCA reaches 81.623%, which effectively removes the primary information of the original data. PCA not only reduces the complexity of the data, but also retains a large amount of crucial information, which is a significant application value for the processing of big data, especially in the fields of data compression and pattern recognition.

An Exploration of the Application of Principal Component Analysis in Big Data Processing

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the arrival of the significant data era, efficiently processing large-scale multidimensional data has become challenging. As a powerful data...

An Exploration of the Application of Principal Component Analysis in Big Data Processing

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 17 gen 2024

Accettato: 23 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0664

Parole chiave
Principal component analysis (PCA), Big data processing, Data dimensionality reduction, Data compression

© 2024 Guo Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

An Exploration of the Application of Principal Component Analysis in Big Data Processing

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 17 gen 2024

Accettato: 23 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0664

Parole chiavePrincipal component analysis (PCA), Big data processing, Data dimensionality reduction, Data compression

© 2024 Guo Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Principal component analysis (PCA), Big data processing, Data dimensionality reduction, Data compression