Centroid Based Clustering

Centroid Based Clustering is located under Textual Analysis () in Clustering, in the left task pane. Use the drag-and-drop method to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis.

Refer to Properties of Centroid Based Clustering.

Properties of Centroid Based Clustering

The available properties of Centroid Based Clustering are as shown in the figure given below.

The table given below describes the different fields present on the properties of Centroid Based Clustering.

Field		Description	Remark
Run		It allows you to run the node.	-
Explore		It allows you to explore the successfully executed node.	-
Vertical Ellipses		The available options are Run till node Run from node Publish as a model Publish code	-
Task Name		It is the name of the task selected on the workbook canvas.	You can click the text field to edit or modify the name of the task as required.
Independent Variables		It allows you to select Independent variables.	You can select more than one variable. You can select any type of variable.
Number of Clusters		It allows you to enter the number of clusters you want to create.	The default value is 8.
Advanced	Method for Initialization	It allows you to select the initialization method.	The available options are k means and random.
	Number of Runs	It allows you to enter the number of times the k-means algorithm will be run with different centroid seeds.	The recommended value is 10.
	Random State	It allows you to enter the value that helps to create clusters.	This parameter is optional.
	Number of Iterations	It allows you to enter the number of times the k-means algorithm will be run with the same centroid seed.	The recommended value is 10.
	Dimensionality Reduction	It allows you to select the method for dimensionality reduction.	The available options are – None and PCA. The default value is None.

Example of Centroid Based Clustering

Consider a textual dataset of musical instruments review. A snippet of input data is shown in the figure given below.

We select the following properties and apply Centroid Based Clustering.

Number of Clusters – 8

Method of Initialization – k-means

Number of Runs – 10

Number of Iterations – 300

The result page is displayed in the figure given below.

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