Hierarchical clustering is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other.
This is one of the types of hierarchical clustering method that introduces “top-down” approach. In this type of approach each observation starts in its own cluster, and pairs of clusters are merged as one move up the hierarchy. In this technique, initially, each data point is considered an individual cluster. At each iteration, the similar clusters merge with other clusters until one cluster or K clusters are formed.
Taken a specific page to analyze:
Now, scraping the site:
Collecting the data after scraping the specific tags:
After collecting the data the putting it into a corpus:
Creating term document matrix:
A given range of terms 1 to 40 with docs 1.
Use in SEO:
Clusters of similar data points actually helps in analyzing tags of a particular site and we can determine how much similar they are. Finding out similar tags which are helpful if the tags are relevant with the site this can increase the visibility of site in SERP.