Use case for Relativity Analytics - Clustering & Categorization

Published September 27, 2013

Relativity Analytics features — including clustering and categorization — allow users to expedite a review by organizing, identifying, and prioritizing documents. This recipe highlights techniques for expediting review using Relativity Analytics, as well as tips for utilizing the Analytics index to identify other hot documents.

Common scenarios

Common scenarios for using clustering and categorization include the following:

  • You are working with a large number of documents. This data may not be coded at all, or it may contain documents that were coded with a multiple choice field for issues.
  • You have limited time to complete the review.
  • You have a limited number of reviewers.


  • Relativity 7.0 or above
  • Relativity Analytics index
  • Workspace access
    • Document – Edit


Scenario 1

You have no prior knowledge of the data set. Clustering in Relativity groups conceptually similar documents without the need for example documents or user input.

Perform the following steps:

  1. Use clustering to automatically organize documents into groups of related data.
  2. Batch and assign documents to reviewers based on the clusters.
  3. Bulk review groups of clustered documents.

Once the above operation is complete, you may find clusters of documents that are clearly irrelevant to your case, such as spam emails. Instead of reviewing hundreds or even thousands of junk emails one at a time, reviewers and system admins can eliminate impertinent documents with minimal time, effort, or subject matter expertise.

Scenario 2

You need to find key documents in an opposing production. Relativity’s categorization functionality identifies and groups similar documents together based on a set of example documents manually identified by the user. When you receive documents in an opposing production, and hot documents have been identified in your documents, you can pass these document values to the new production items by making them examples for the Analytics engine.

Perform the following steps:

  1. Create a categorization set and use the Issue Designation multiple choice field as the Categories and Examples source.
  2. Use the Synchronize feature for your new categorization set.
  3. Click Categorize All to categorize the opposing counsel's documents with your categorization set and to identify opposing counsel's documents which are similar to your hot documents.

Setting the available Categories and Examples source option to use your multiple choice designation field enables the Synchronize feature for categorization. The Synchronize feature automatically creates categories for all choices associated with the specified field and designates example records for all documents with this field coded. With the example document records identified in your data set, categorization identifies and organizes similar documents in the opposing counsel's data set.


Creating an optimized Relativity Analytics Index