k-Means Clustering to Enhance SEO: A Data Driven Approach
Abstract: In relation to Data Mining, Search Engine Optimisation is associated with applying rigorous analysis to datasets which encompass data such as performance in terms of content and user behaviour. The goal is to convert this data into actionable insights to improve page rank and increase organic traffic. It is of utmost importance for webmasters to optimize SEO factors to satisfy the search engines and thereby attain more visibility. This paper introduces a novel approach to draw valuable, reliable, and practical insights from Google Search Console using k-Means Clustering to draw focus on the influence of various SEO factors. The factors taken from Google Search Console include Page, Query, Clicks, Impressions, CTR, and Position. Other parameters like length of the title and Meta Description should be included from web crawlers. The clustering technique enables one to gain insight into which parameters should be optimised for maximum impact.
Keywords: Data Mining, Search Engine Optimisation, k-Means Clustering, Google Search Console, SEO factors
How to Cite?: Meghana Mukunda Joshi, "k-Means Clustering to Enhance SEO: A Data Driven Approach", Volume 9 Issue 6, June 2020, International Journal of Science and Research (IJSR), Pages: 550-553, https://www.ijsr.net/getabstract.php?paperid=SR20607192344, DOI: https://dx.doi.org/10.21275/SR20607192344