Skip to main content

Random Clustering

Synopsis​

This operator performs a random flat clustering of the given ExampleSet. Clustering is concerned with grouping objects together that are similar to each other and dissimilar to the objects belonging to other clusters.

Description​

This operator performs a random flat clustering of the given ExampleSet. Please note that this algorithm does not guarantee that all clusters will be non-empty. This operator creates a cluster attribute in the resultant ExampleSet if the add cluster attribute parameter is set to true. It is important to note that this operator randomly assigns examples to clusters, if you want proper clustering please use an operator that implements a clustering algorithm like the K-Means operator.

Clustering is concerned with grouping together objects that are similar to each other and dissimilar to the objects belonging to other clusters. Clustering is a technique for extracting information from unlabeled data. Clustering can be very useful in many different scenarios e.g. in a marketing application we may be interested in finding clusters of customers with similar buying behavior.

Input​

example set​

The input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process. The output of other operators can also be used as input.

Output​

cluster model​

This port delivers the cluster model which has information regarding the clustering performed. It tells which examples are part of which cluster.

clustered set​

The ExampleSet that was given as input is passed with minor changes to the output through this port. An attribute with id role is added to the input ExampleSet to distinguish examples. An attribute with cluster role may also be added depending on the state of the add cluster attribute parameter.

Parameters​

Add cluster attribute​

If enabled, a new attribute with cluster role is generated directly in this operator, otherwise this operator does not add the cluster attribute. In the latter case you have to use the Apply Model operator to generate the cluster attribute.

Add as label​

If true, the cluster id is stored in an attribute with the label role instead of cluster role (see add cluster attribute parameter).

Remove unlabeled​

If set to true, unlabeled examples are deleted.

Number of clusters​

This parameter specifies the desired number of clusters to form. There is no hard and fast rule for the number of clusters to form. But, generally it is preferred to have a small number of clusters with examples scattered (not too scattered) around them in a balanced way.

Use local random seed​

This parameter indicates if a local random seed should be used for randomization.

Local random seed​

This parameter specifies the local random seed. This parameter is only available if the use local random seed parameter is set to true.