common email threading problems

Several common problems may cause the message count to be miscalculated in email threading analytics.

Extra text above an email header:

An embedded email link or attachment:

Confidential footers or extra email addresses at the end of a root email can also cause errors.

Reviewing documents with conceptual indexes in Relativity

When a Relativity admin creates a conceptual index, it will enable five different means of reviewing documents in a workspace to be performed.

1. Categorization sets can be created that use a set of example documents to find conceptually similar documents.

2. You can create clusters of conceptually similar documents without the same user input required with categorization. A few documents can be selected without assembling sets of example documents, or defining categories.

3. Concept searching can be run simply by selecting a block of text and searching across the workspace for documents with similar conceptual content.

4. Keyword expansion will find terms that are conceptually related to a selected term.

5. In the document viewer, you'll be able to select 'Find Similar Documents' to find documents related to the one you are reviewing.

Predicting the number of top level clusters

In a recent Relativity webinar, it was contended that when performing clustering on a saved search, a generality setting of 0.5 creates 8 top-level clusters in the example data set. The instructor noted that this setting is not guaranteed to generate 8 on all data sets. Generality at 0.9 creates 4 top-level clusters in the same data set.

In a very general way, this seems to borne out in my own Relativity sandbox workspace. Clustering a saved search at 0.5 generality . . .

. . . doesn't create 8 top level clusters in my data set, but it does create six large top level clusters, plus several other top clusters which might be grouped together to form two additional top level clusters of similar size.

A generality setting of .9 . . . .

. .. . won't create 4 top level clusters in my completely different data set:

. . . but it does create four top level clusters clearly larger than the others.

It's a general rule of thumb but not an entirely unuseful one, however my test clusters seem to refute the general rule that high generality settings will lead to fewer top-level clusters.