With Rule-Based Classification, you write the rules for classifying documents yourself. With Supervised Classification, Oracle Text writes the rules for you, but you must provide a set of training documents that you preclassify. With unsupervised classification (also known as clustering), you do not have to provide a training set of documents.

Clustering is performed with the CTX_CLS.CLUSTERING procedure. CTX_CLS.CLUSTERING creates a hierarchy of document groups, known as clusters, and, for each document, returns relevancy scores for all leaf clusters.

For example, suppose that you have a large collection of documents about animals. CTX_CLS.CLUSTERING creates one leaf cluster about dogs, another about cats, another about fish, and a fourth about bears. (The first three might be grouped under a node cluster about pets.) Suppose further that you have a document about one breed of dogs, such as Chihuahuas. CTX_CLS.CLUSTERING assigns the dog cluster to the document with a very high relevancy score, whereas the cat cluster is assigned a lower score and the fish and bear clusters are still assigned lower scores. After scores for all clusters are assigned to all documents, an application can then take action based on the scores.

As noted in "Decision Tree Supervised Classification", attributes used for determining clusters may consist of simple words (or tokens), word stems, and themes (where supported).

CTX_CLS.CLUSTERING assigns output to two tables (which may be in-memory tables):

• A document assignment table showing the document’s similarity to each leaf cluster. This information takes the form of document identification, cluster identification, and a similarity score between the document and a cluster.

• A cluster description table containing information about a generated cluster. This table contains cluster identification, cluster description text, a suggested cluster label, and a quality score for the cluster.

CTX_CLS.CLUSTERING uses a K-MEAN algorithm to perform clustering. Use the KMEAN_CLUSTERING preference to determine how CTX_CLS.CLUSTERING works.