---
sourceDocument: Xanadu Enable AI
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/xanadu/intelligent-experiences

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ft:locale :

    - en-US

ft:publication_title :

    - Xanadu Enable AI

ft:clusterId :

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---

# Configure Connect Component algorithm and Levenshtein Distance method for a clustering solution

# Configure Connect Component algorithm and Levenshtein Distance method for a clustering
solution {#ariaid-title1}

* Release version: Xanadu
* 
* Updated February 11, 2025
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 1 minute to read

Apply Configure Connect Component and Levenshtein Distance method encoding to
optimize the training for your clustering solutions.

## Before you begin

Role required: admin or ml_admin  
Note:  
Configuring advanced settings on your ML solutions is optional. If you choose to configure any of these settings, make sure you're well informed regarding the technology you're enabling in the solution, and that your use case benefits from what the technology offers. For more information, see the [Dive deeper with Clustering Advanced Parameters](https://www.servicenow.com/community/intelligence-ml-articles/dive-deeper-with-clustering-advanced-parameters/ta-p/2695847) article on ServiceNow Community.

* Create and train a clustering solution definition or use an existing one.
* Role required: admin or ml_admin
{#configure-connect-component-levenshtein-distance-for-clustering__ul_wxv_kxs_1kb}

## About this task

When training clustering solutions, you have the following three options.  
* Use the default k-means algorithm.
* Use the optional DBSCAN solution parameter with the Euclidean distance method as a metric.
* Use the optional DBSCAN, Minimum Neighbors, and Levenshtein Distance solution parameters. Connect Component is enabled by DBSCAN and Minimum Neighbors, and supports both Paragraph Vector-based text and Levenshtein Distance-based text. If you train your solution using the Levenshtein Distance method, you don't need to use a word corpus in your clustering solution.
{#configure-connect-component-levenshtein-distance-for-clustering__ul_pwr_qq1_plb}

In this example scenario, you train your solution definition by using the third
option referenced above.

## Procedure

1. Navigate to AllPredictive IntelligenceClusteringSolution Definitions.
2. Open a trained clustering solution definition form.
3. On the Advanced Solution Settings tab in the Related Links section of the form, select New.  
4. Create a parameter record.
   1. In the Solution Parameters field, select the search icon.
   2. In the ML Solution Parameters screen, select Levenshtein Distance.

   {#configure-connect-component-levenshtein-distance-for-clustering__substeps_ow5_5tj_zjb}  
5. Select Submit.  
   The Advanced Solution Setting record screen refreshes.

6. Select Submit.  
   Result: Levenshtein Distance is configured for your clustering solution. Its solution parameter appears on the Advanced Solution Settings tab of your clustering definition form.  
7. Repeat steps 1-6 from the previous Levenshtein Distance example, except this time you're creating the Minimum Neighbors and DBSCAN solution parameters, which together enable the Connect Component feature.  

   When you select, configure, and submit the Minimum Neighbors solution parameter, be sure to set the
   User Inputs field with a value of
   <kbd class="ph userinput">1</kbd>. Only some parameters have a User Inputs
   field.  
   Result:

   Connect Component is configured for your clustering solution. Its two
   solution parameters appear on the Advanced Solution Settings tab of your
   clustering definition form, alongside the Levenshtein Distance parameter you
   configured in steps 1-6 of this procedure.  
**Related tasks**   

* [Create and train a clustering solution](https://servicenow-prod.fluidtopics.net/W5fuNvL5qZrUYelBzXRshw "Group similar records into clusters so you can address them collectively or identify patterns.")

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