Breaking Down SEO Complexity: Bridging PCA and Bayesian-Optimized t-SNE

Conference paper

Karami, A., Ghasemabadi, S. F. and Amirhosseini, M. 2024. Breaking Down SEO Complexity: Bridging PCA and Bayesian-Optimized t-SNE. 2024 IEEE International Conference on Big Knowledge (ICBK). IEEE.
AuthorsKarami, A., Ghasemabadi, S. F. and Amirhosseini, M.
TypeConference paper
Abstract

The complexity of Search Engine Optimization (SEO) data requires sophisticated analytical tools. This study integrates Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), optimized by Bayesian methods, to enhance SEO data analysis. PCA is a technique that minimises the number of dimensions in data, allowing for the identification of important aspects related to search engine optimisation (SEO). On the other hand, optimised t-SNE gives a visual representation of data clustering and correlations in a way that is easy to understand and interpret. Our methodology enhances computing efficiency and interpretability, surpassing conventional techniques in analysing both linear and non-linear data. The results develop more strategic decision-making in the field of SEO, indicating a remarkable advancement in SEO analytics.

Year2024
Conference2024 IEEE International Conference on Big Knowledge (ICBK)
PublisherIEEE
Accepted author manuscript
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Publication process dates
Accepted07 Sep 2024
Deposited07 Feb 2025
Journal citationp. In press
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/1821544/all-proceedings
Copyright holder© 2024 The Author
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