General Information

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms.

The objective of K-means brick is to group similar data points together and discover underlying patterns. To achieve this objective, K-means looks for a fixed number (k) of clusters in a dataset.

Description

Brick Location

Bricks → Analytics → Data Mining / AI → Clustering → K-Means Clustering

Brick Parameters

• Number of clusters

  Amount of segments the data should be split into (k).

• Columns to exclude

  List of columns that are going to be excluded from the analysis. These columns will be passed to the output dataset. It is possible to choose several columns by clicking on the '+' button in the brick settings.

• Remove all except selected

  If the checkbox is on, only the selected columns will be considered, otherwise, they will be filtered out from the analysis.

Brick Inputs/Outputs

• Inputs

  Brick takes the dataset.

• Outputs

  Brick produces the dataset, with the additional column "predicted_cluster".

Example of usage

Let's consider the dataset from the binary classification problem ‣. The general information about the dataset is represented below:

• passengerid (category) - ID of passenger
• name (category) - Passenger's name
• pclass (category) - Ticket class
• sex (category) - Gender
• age (numeric) - Age in years