Let’s look at the technology Google uses to crawl pages and determine ranking on SERPs.

Google’s AI is able to identify a sub-entry point to randomly select the next page to be crawled.  It does this by using a selection algorithm that operates based on past crawling data.  (The crawl schema can be carried out using past crawling data.  Pages can be part of multiple content clusters, and clusters can be associated with multiple categories.)  When no new host-names are discovered, the crawl cycle functions like an infinite loop, constantly adjusting the probability that any given classification of a page is correct.  Bear in mind that several clusters will probably exist for the same domain, as any given website can encompass multiple business proposes.  Moreover, clusters can move from one status to another as new URLs are discovered.

A URL processing mechanism can be thought of as having three parts: a crawler, a clusterizer, and a publisher.  The crawler fetches the host, the subdomain, and the subdirectory.  To understand how this works, one must distinguish between two types of crawling: progressive and incremental.  Progressive crawling collects data from a subset of pages included in a cluster within a specific domain.  Incremental crawling focuses on the additional pages within the known crawl-space before fetching.  Thus new pages can be discovered during the crawling process.

Google’s clusterizer adds pages to content clusters until there are no new pages to classify; or until it is sufficiently developed to play its desired role.  A cluster is considered “mature” when its categorization has been deemed salient (when certain thresholds are met or when different clusters containing the same URL are classified identically).  The classification of a cluster as mature or immature converges when no new hostnames are discovered.  This is open-ended as new URLs are discovered.  The status is weighted based on a confidence level derived from the cluster’s rate of growth.  Larger numbers of pages on a similar topic tend to mature a content cluster.

The idea is to perform content optimization strategy around topic hubs.  Creating pages on the same topic will grow a cluster to maturity and thus help improve the position of a page on the SERPs.  Related content on a given topic can be advantageous, because it increases the probability that a categorization is correct.  It then ensures that a cluster is made available as an answer to search queries.

Search intent, whether informational (looking for a job), navigational (finding a local business), or transactional (shopping) plays an important role.  Clusters and sub-clusters identify and group URLs that meet these sorts of intentional queries.  As clusters are composed of related content, an analysis of a website using AI tools can determine the probability of relevant clustering on the website; and thereby predict where additional content can encourage cluster growth and facilitate the maturation of clusters.

Different kinds of content clusters exist.  The aim of Primary clusters, which tend to be highly variegated, is indeterminate–as with vast SOCIAL clusters like Facebook and Twitter.  Secondary clusters–as with sub-clusters like the jobs section of LinkedIn (a job cluster resides within the primary social cluster).  There are also geographic clusters.  Depending on the subscriber location, a specific clusterization can be applied–as with Yelp.

A hierarchical clustering algorithm is a kind of similarity matrix for the different clusters. The matrix finds the closest pair of clusters to merge, so the number of clusters tends to reduce.  This logic can be repeated as many times as necessary.  Clusterization is invariably influenced by geographic location–which is why SEOMachineAI’s LOCAL arm comes in very handy.

The notion of content clusters is about more than just determining in which cluster a specific page might belong.  Content clusters are ideally defined so as to correspond to intentions.  A well-crafted clustering algorithm finds groups that are related but have not been explicitly labeled as related.  In the case of content clusters, each paragraph of text will form a centroid.  Every correlated centroid will then be grouped together.

“Publishing” can refer to any number of techniques to approve, reject, or adjust clusters.  A publisher is effectively the gateway to SERP content.  At the end of the day, the publisher determines if a cluster can be promoted to its greatest coverage–that is, as an answer on the SERPs.

AI tools can be used to adjust the scheme of clusterization.  In trying to understand the themes of the pages in a cluster, the system can evaluate whether the cluster might answer search queries.  This makes it difficult to “game the system” by using conventional strategies like keyword stuffing.  Making use of AI technology is about uncovering important clues that might inform a savvy SEO strategy.