Density Based Clustering

Density 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 Density Based Clustering.

Properties of Density Based Clustering

The available properties of Density Based Clustering are shown in the figure below.

The table given below describes the different fields present on the properties of Density 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.
Text		It allows you to select Independent variables.	You can select more than one variable. You can select any type of variable.
Advanced	Epsilon	It allows you to enter the maximum distance between two data points for them to be considered in the neighborhood of each other.	The default value is 0.5
	Minimum Number of Samples	It allows you to enter the minimum number of samples to be considered while assigning clusters.	The default value is 10.
	Algorithm	It allows you to select the algorithm to be used for searching the nearest neighbor while assigning clusters.	The available options are – Auto – It determines the algorithm among Ball_tree, Kd_tree, and Brute that is best suited for the input dataset. Ball_tree – It divides the data points into two clusters, and each cluster is contained by either a circle or a sphere. Kd_tree – It divides the data points into two sets at each node. Brute – It uses the brute-force method to compute distances between all pairs of data points.

Example of Density Based Clustering

Consider a dataset of Musical Instruments review. A snippet of input data is shown in the figure given below.

After using the Density Based Clustering, the following results are displayed.

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As seen in the above figure, each cluster's size is mentioned along with the Silhouette Score.

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