Machine Learning / Initialize Model / Clustering

 

Updated: June 27, 2015

What is Clustering?

Clustering algorithms are algorithms that learn to group a set of items together based on a set of features. For example, clustering is often used in text analysis to group pieces of text that contain common words together.  Clustering can be used to group unlabeled data by figuring out which data points are closest together, and then determining the centroid, or central point, of each grouping. Once the algorithm is trained, it can be used to predict which cluster an instance of data belongs to.

Wondering which algorithm you need for a task? See these topics:

The category for Initialize/Clustering includes the following modules:

Module

Description

K-Means Clustering 

Configures and initializes a K-means clustering model

If you want to use a different clustering algorithm, or want to create your own clustering module using an R package, see these topics:

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