Clustering and Unsupervised Learning in SwiftUI

Learn how to discover patterns in your data without having pre-existing labels or classification

Photo by Jon Tyson on Unsplash

SwiftUI is a framework for building user interfaces on Apple platforms. With its easy-to-use syntax, developers can create powerful and interactive user interfaces with less code. One area where SwiftUI excels is in data visualization. In this article, we will explore the concept of clustering and unsupervised learning in SwiftUI and demonstrate how to implement it in code.

Clustering is a technique used in unsupervised machine learning to group similar data points into clusters. This is useful when dealing with large amounts of data where it’s difficult to identify patterns or relationships. In SwiftUI, we can use the K-Means algorithm to perform clustering. This algorithm is widely used for clustering and is relatively simple to understand and implement.

Let's begin.

To get started, let’s first import the required libraries. In this case, we will use the Swift Machine Learning Library, which provides a convenient interface for implementing machine learning algorithms in Swift.

import SwiftML
import SwiftUI