Connectivity Based Clustering

Connectivity 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 Connectivity Based Clustering.

Properties of Connectivity Based Clustering

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

The table given below describes the different fields present on the properties of Connectivity 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		It allows you to explore the successfully executed node.	-
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.
Number of Clusters		It allows you to enter the number of clusters you want to create.	The default value is 8.
Advanced	Linkage Metric	It allows you to select the metric used to compute the linkage.	The available options are - Euclidean, L1, L2, Manhattan, Cosine, or Precomputed. If linkage Criterion is Ward, only Euclidean is accepted. For Precomputed, a distance matrix (instead of a similarity matrix) is needed as input for the fit method.
	Linkage Criterion	It allows you to select the metric used for the merge strategy.	The available options are – Ward – Minimizes the sum of squared differences within all clusters. Complete – Minimizes the distance between data points of pairs of clusters. Average – Minimizes the average distance between all observations of pairs of clusters.

Example of Connectivity Based Clustering

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

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

As seen in the above figure, the number of clusters and each cluster's size are displayed along with the Silhouette Score.

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