Lemma-Based Clustering and Serp-Based Clustering

Lemma-based clustering

Prior to the keyword clustering, search engine optimization experts developed keyword grouping tools based on the process known as lemmatization. Lemma is a base or dictionary form of a word (without inflectional endings). In linguistics, lemmatization is a process of grouping together the different inflected forms of a word so they can be analyzed as a single item.

In search engine optimization, the process of lemmatization includes four steps:

1. Keywords are picked from the list one-by-one;
2. Keywords are broken down into lemmas;
3. Keywords with the same lemmas are detected;
4. Keywords with matching lemmas are grouped together.

As a result, a search engine optimization specialist gets a list of keyword groups. Each keyword in a certain group has matching lemmas with all other keywords within this group.

In short, in SEO it will work like this:

1. The tool will take your keywords
2. Break them down to the lemmas
3. Find keywords with the same lemmas
4. Group them together

Here’s a fragment of the list you’ll finally get:

SERP-based clustering

SERP-based keyword clustering produces groups of keywords that might reveal no morphological matches, but will have matches in the search results. It allows search engine professionals getting a keyword structure close to what a search engine dictates.

How Does SERP-based Keywords Clustering Work?

SERP based keyword grouping should be of primary interest for those who want to implement topic targeting. As you might have guessed by the title, the keywords will be segmented into groups based on the search results. In particular, on the TOP 10 of the search results.

Here’s a detailed four-step algorithm:

1. The tool takes keywords from a list and sends them as the queries to the search engine. Then it scans the TOP 10 results for each keyword.
2. If the search engine displays the same web pages for different keywords and there are several matches, these keywords will be banded together.
3. The number of matches that triggers grouping of two different keywords (clustering level) is customizable.
4. The keywords that revealed no matches in the search results are segmented into a separate group.

Automatic keywords clustering, as opposed to manual keywords grouping, consists in analyzing search engines page results for all keywords you’ve collected, and identifying matching URLs.

For example, let’s say “piano brands” and “Steinway & Sons piano” are among the keywords you’ve collected during your keyword research, a keywords clustering tool would search “best pianos brand” and “best piano makers” on Google. It would then detect that there are a few matching URLs for those two search queries within the top 10 results, and would conclude that “best piano brand” and “best piano makers” belong to the same keyword cluster.

Matching URLs for “best piano brand” and “best piano makers” on SERP

Most keywords clustering tools allow you to adjust the clustering level (also called clustering degree), which is how many matching URLs you need to find to group two keywords together. 

There are three clustering levels: soft, medium and strong.

• Soft clustering level requires a minimum of 3 matching URLs within the top search results
• Medium clustering level requires at least 5 matching URLs within the top search results
• Strong clustering level requires a minimum of 7 matching URLs.

The stronger the clustering level, the lower the number of keywords each group will contain.