Clustering

Overview

Clustering groups similar objects together, ensuring items within a group are more alike than those in other groups. Clustering is a type of unsupervised learning. Unsupervised learning is a technique in which you can draw inferences from datasets consisting of data without labeled responses.

You can use this Snap determine the intrinsic grouping among unlabeled numeric data. If your data has categorical fields, the Snap ignores all such fields.


Clustering Snap Settings

Prerequisites

None.

Limitations

None

Known issues

None

Snap views

View Description Examples of upstream and downstream Snaps
Input

Accepts one input view that provides the dataset for clustering:

  • Input1: The primary input dataset.
Output

Produces one output view containing the clustering results:

  • Output1: Contains the assigned cluster IDs for each data point.
Error

Error handling is a generic way to handle errors without losing data or failing the Snap execution. You can handle the errors that the Snap might encounter when running the pipeline by choosing one of the following options from the When errors occur list under the Views tab. The available options are:

  • Stop Pipeline Execution Stops the current pipeline execution when an error occurs.
  • Discard Error Data and Continue Ignores the error, discards that record, and continues with the remaining records.
  • Route Error Data to Error View Routes the error data to an error view without stopping the Snap execution.

Learn more about Error handling in Pipelines.

Snap settings

Legend:
  • Expression icon (): JavaScript syntax to access SnapLogic Expressions to set field values dynamically (if enabled). If disabled, you can provide a static value. Learn more.
  • SnapGPT (): Generates SnapLogic Expressions based on natural language using SnapGPT. Learn more.
  • Suggestion icon (): Populates a list of values dynamically based on your Account configuration.
  • Upload : Uploads files. Learn more.
Learn more about the icons in the Snap settings dialog.
Field / field set Type Description
Label String

Required. Specify a unique name for the Snap. Modify this to be more appropriate, especially if more than one of the same Snaps is in the pipeline.

Default value: Clustering

Example: Cluster data
Algorithm String/Suggestion

The clustering algorithm used to group data into specific clusters. The available options are:

  • K-Means: Partitions n observations into k clusters, where each observation belongs to the cluster with the nearest mean.
  • X-Means: An extended K-Means algorithm that automatically determines the number of clusters using Bayesian Information Criterion (BIC) scores.
  • G-Means: Another extended K-Means algorithm that determines the number of clusters based on a normality test.

Default value: K-Means

Example: X-Means
Max Cluster Integer/Expression

Enter the maximum number of clusters that the Snap must create.

Default value: 3

Example: 5
Pass Through Checkbox

Includes the original dataset in the output along with the cluster assignments.

Default status: Selected
Snap execution Dropdown list Select one of the three modes in which the Snap executes.
Available options are:
  • Validate & Execute. Performs limited execution of the Snap and generates a data preview during pipeline validation. Subsequently, performs full execution of the Snap (unlimited records) during pipeline runtime.
  • Execute only. Performs full execution of the Snap during pipeline execution without generating preview data.
  • Disabled. Disables the Snap and all Snaps that are downstream from it.