MATLAB Implementation of Unsupervised Clustering Algorithm

Unsupervised clustering algorithms are capable of automatically grouping data without requiring pre-specified cluster numbers, while achieving higher clustering accuracy than traditional clustering methods. These algorithms have extensive applications across various domains for data analysis and pattern recognition tasks. They help uncover hidden patterns and correlations within datasets, extracting valuable insights from raw data. Consequently, unsupervised clustering algorithms hold significant importance in data science and machine learning fields. Typical MATLAB implementation involves key functions such as kmeans() for centroid-based clustering or clusterdata() for hierarchical approaches. Advanced techniques may include density-based clustering using DBSCAN implementation or Gaussian mixture models with fitgmdist(). The algorithm typically initializes with random cluster centers, iteratively assigns data points to nearest clusters using distance metrics like Euclidean or Manhattan distance, and updates centroids until convergence criteria are met. Code implementation often includes optimization for handling high-dimensional data through dimensionality reduction techniques like PCA before clustering.