Data Science Algorithms for Unsupervised Learning: CLUSTER ANALYSIS AND KNN CLASSIFIERS. Examples with MATLAB

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Unsupervised learning is a powerful tool in data science that allows us to discover patterns and relationships within data without the need for labeled outcomes. Cluster analysis and K-nearest neighbors (KNN) classifiers are two popular algorithms used in unsupervised learning. In this post, we will explore these algorithms and provide examples of implementing them in MATLAB.

Cluster analysis is a method of grouping similar data points together based on certain criteria. One common algorithm used for cluster analysis is K-means clustering. K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. Let’s take a look at an example of implementing K-means clustering in MATLAB:

% Generate sample data<br />
data = rand(100, 2);<br />
<br />
% Perform k-means clustering<br />
k = 3; % Number of clusters<br />
[idx, centroids] = kmeans(data, k);<br />
<br />
% Plot the data points and centroids<br />
figure;<br />
gscatter(data(:,1), data(:,2), idx);<br />
hold on;<br />
plot(centroids(:,1), centroids(:,2), 'kx', 'MarkerSize', 15, 'LineWidth', 3);<br />
```<br />
<br />
K-nearest neighbors (KNN) is a simple and effective algorithm for classification tasks in unsupervised learning. In KNN, the class of a data point is determined by a majority vote of its k nearest neighbors. Let's see how we can implement KNN classification in MATLAB:<br />
<br />
```matlab<br />
% Generate sample data<br />
data = rand(100, 2);<br />
labels = randi([1, 3], 100, 1); % Random labels for classification<br />
<br />
% Create a KNN classifier<br />
knn = fitcknn(data, labels);<br />
<br />
% Predict the class of a new data point<br />
new_data = [0.5, 0.5];<br />
predicted_class = predict(knn, new_data);<br />
disp(['Predicted class: ' num2str(predicted_class)]);<br />
```<br />
<br />
In this post, we have explored the concepts of cluster analysis and KNN classifiers in unsupervised learning and provided examples of implementing these algorithms in MATLAB. These algorithms can be powerful tools for uncovering hidden patterns and relationships within data without the need for labeled outcomes.

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