Unsupervised learning is a type of machine learning where the algorithm learns patterns and structures from the input data without being explicitly told what to look for. In other words, the algorithm is not given labeled data with predefined outcomes, but rather it identifies patterns and relationships in the data on its own.
There are several techniques used in unsupervised learning, including clustering, dimensionality reduction, and association rule learning. Clustering algorithms group similar data points together based on their characteristics, while dimensionality reduction techniques reduce the number of features in a dataset without losing important information. Association rule learning algorithms discover relationships between variables in the data.
In this tutorial, we will focus on clustering algorithms, particularly the K-means algorithm. K-means is a popular unsupervised learning algorithm that groups data points into clusters based on their similarities.
To demonstrate how K-means works, let’s create a simple example using HTML and JavaScript. We will generate some random data points and apply the K-means algorithm to cluster them.
First, let’s create an HTML file and define the structure of the page:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>K-means Clustering</title>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
</head>
<body>
<h1>K-means Clustering Example</h1>
<canvas id="myChart"></canvas>
</body>
</html>Next, let’s add some JavaScript code to generate random data points and apply the K-means algorithm:
<script>
// Generate random data points
const data = [];
const numPoints = 100;
for (let i = 0; i < numPoints; i++) {
const x = Math.random() * 100;
const y = Math.random() * 100;
data.push({ x, y });
}
// Apply K-means clustering
const numClusters = 3;
const kmeans = new KMeans(numClusters, data);
const clusters = kmeans.run();
// Display the clustered data on a scatter plot
const ctx = document.getElementById('myChart').getContext('2d');
const scatterChart = new Chart(ctx, {
type: 'scatter',
data: {
datasets: clusters.map((cluster, index) => ({
label: `Cluster ${index + 1}`,
data: cluster,
pointBackgroundColor: `hsl(${index * (360 / numClusters)}, 70%, 50%)`,
})),
},
});
</script>In this code snippet, we first generate 100 random data points and then apply the K-means algorithm with 3 clusters. Finally, we display the clustered data on a scatter plot using the Chart.js library.
You can run this HTML file in a browser to see the K-means clustering in action. Feel free to experiment with different numbers of data points and clusters to see how the algorithm behaves.
In conclusion, unsupervised learning is a powerful technique in machine learning that allows algorithms to discover patterns and structures in data without explicit guidance. Clustering algorithms like K-means are commonly used in unsupervised learning for tasks such as customer segmentation, anomaly detection, and image compression. By understanding and implementing unsupervised learning algorithms, you can unlock valuable insights from your data and make more informed decisions in various domains.
