A neighborhood graph is built for each of the selected clusters.

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The algorithm is based on the idea of ​​representing data in the form of vectors. It assumes that each word, pixel in an image, or other data is assigned a numerical value representing it (Nguyen, 2019). Therefore, in the first step, all data must be transformed into vectors.

Then the data vector space is divided into K clusters. It should be noted that the hierarchical clustering technique is used for this purpose, which allows for reducing the time complexity of this operation.

In the next step, clusters are selected that are in the nearest query neighborhood. The number of clusters is dynamically adjusted to the characteristics of the data (Chen, 2021).

The final stage of the algorithm is to perform a search operation for each of the adjacency graphs. This operation starts at the established starting points. The actions described in the algorithm are performed in an iterative manner (Microsoft, 2022a).

Why is Bing interesting?
The overall market share of the Bing search engine is not colombia whatsapp data large, but the search engine has other distinguishing features that make it worth taking an interest in from an SEO (Search Engine Optimization) perspective and optimizing the websites it administers accordingly. The most important of these include less SERP (Search Engine Result Page) competition and explicit criteria for positioning search results (Mmeje, 2021).

Less SERP competition
A significant portion of businesses focus primarily on improving the visibility of their websites in Google search results, often omitting other search engines. For this reason, competition in the case of Bing may be much smaller. This in turn makes obtaining a high position in search results for selected keywords much easier than in the case of the Google search engine.

Explicit criteria for positioning search results