Overview of Cluster Discovery

A cluster is a collection of relevant terms, providing a grouping of Endeca records that share these common terms.

All of the terms (which are dimension values) must come from the same dimension, which must be a Term Discovery dimension. Clusters can be generated only if the Term Discovery dimension is available for refinements. So not only can this dimension not be hidden, it must also be available from the navigation states for which clusters are desired. Your application must therefore have this dimension globally available (rather than having it available only when triggered by precedence rules).

The following features apply to the clusters:

The Endeca MDEX Engine performs dynamic clustering. That is, when a user navigates the clustering tree, it is reclustered at any selection, allowing users to zoom into their data to practically any level.
There is no limit in the number of records that can be clustered.
Each cluster is represented by a list of terms, which provide to the user what is known as information scent: the user is instantly aware of what each cluster contains (that is, the user can quickly understand the implied content of the clustered records).
All clusters are designed to maximize two metrics: coherence (each cluster has only closely related records) and distinctiveness (two different clusters will have different records).
Each cluster has high recall. A match partial technique is typically used on cluster selection, maximizing the number of semantically related records that are returned.

Multiple clusters can be generated from the same dimension. You can configure the maximum number of clusters that can be generated by using the Clusters tab on the Dimensions editor. This tab also allows you to set several parameters for cluster generation. For details, see Chapter 3 (“Configuration Guidelines for Clustering”).

Programmatically, a cluster is a Supplementobject that accompanies the result of a navigation query. For details on the object’s properties and how to display them, see the “Displaying clusters” topic in Chapter 5.