MATLAB Code Implementation for Clustering

In this document, I would like to introduce the method of implementing clustering using MATLAB, and I am highly satisfied with its performance. This implementation incorporates built-in sample datasets that allow for immediate execution with a simple click. The key advantage is that you can quickly and accurately observe clustering outcomes and perform subsequent analysis and interpretation. The solution leverages MATLAB's clustering algorithms such as k-means (using the kmeans function), hierarchical clustering (linkage and cluster functions), or DBSCAN, which help identify natural groupings within the data. By utilizing MATLAB for clustering analysis, you can gain deeper insights into data relationships and uncover underlying patterns and trends. The implementation includes proper data preprocessing steps, distance metric selection (Euclidean, Manhattan, etc.), and cluster validation techniques to ensure robust results. This approach has demonstrated widespread applicability and effectiveness across both academic research and practical applications, particularly in fields like pattern recognition, data mining, and machine learning.