Centroid Based Clustering

Description

In Centroid Based Clustering, a central vector represents each cluster. The objects are assigned to the clusters such that the squared distance between the object and the central vector is minimized.

Why to use

To convert textual data to its numerical form.

When to use

  • When the number of clusters is known.
  • When each cluster size is expected to be of equal size.

When not to use

  • When data is labeled.
  • When the number of clusters is not known.
  • When clusters are not to be of equal size.

Prerequisites

Input data should be of text type and should not contain special characters and numbers.

Input

Textual Data

Output

Data divided into clusters

Statistical Methods used

  • K-means
  • Random Initialization

Limitations

  • The number of clusters needs to be known.
  • Not very robust to outliers.
  • Does not work very well with non-convex shapes.
  • Tries to generate equal-sized clusters.

Centroid Based Clustering is located under rubitext ( ) 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.

Figure: Centroid Based Clustering

In Centroid-based clustering, each cluster is represented by a central vector. The central vector may not necessarily be a part of the dataset. A data value is assigned to a cluster depending upon its proximity, such that its squared distance from the central vector is minimized.

The k-means algorithm is the most widely used centroid-based clustering algorithm. In this algorithm, the dataset is divided into k pre-defined, distinct, and non-overlapping clusters. Each data point is assigned to a cluster such that the arithmetic means of all data points within a cluster is always minimum. Minimum variation within a cluster ensures greater homogeneity of data points within that cluster.

Properties of Centroid Based Clustering

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

Figure: Properties of Centroid Based Clustering

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

Table: Description of Fields present on the Properties of Centroid Based Clustering

Field

Description

Remark

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.

Figure: Input Data Snippet 


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.

Figure: Output of Centroid Based Clustering
Table of Contents