Data Clusters in Machine Learning: Explained

• A cluster is a collection of similar data.
• Clustering is a type of unsupervised learning.
• The correlation coefficient describes the strength of the relationship.

Data clusters

Explore data clusters in machine learning, including the key concepts, practical examples, important considerations, and useful guidance.

Data points grouped together in a chart can often be categorized into clusters.

In the diagram below, we can distinguish three different clusters:

Identify data clusters

Clusters can contain a wealth of valuable information, but they come in many different shapes, so how can we recognize them?

The two main methods are:

• Use visualization
• Using clustering algorithms

Clustering

Clustering is a type of unsupervised learning.

Clustering aims to:

• Collect similar data into groups.
• Collect dissimilar data into other groups.

Clustering methods

• Density method
• Hierarchical method
• Partitioning method
• Grid-based method

Density methods consider points in high-density regions to have more similarities and differences compared to points in lower-density regions. Density methods have good accuracy. They also have the ability to merge clusters. Two popular algorithms are DBSCAN and OPTICS.

Hierarchical clustering methods create clusters in a tree-like structure. New clusters are formed by utilizing previously formed clusters. Two popular algorithms are CURE and BIRCH.

Grid-based methods format data into a finite number of cells forming a grid structure. Two common algorithms are CLIQUE and STING.

Partitioning methods divide objects into k clusters, with each partition forming a cluster. A common algorithm is CLARANS.

Correlation coefficient

The correlation coefficient (r) describes the strength and direction of the linear relationship between the x/y variables on the scatter plot.

The value of r is always between -1 and +1:

Full uphill slope +1.00:

A full drop to -1.00:

Strong upward slope of +0.61:

There is no linear relationship: