Implementation of Hierarchical Clustering Algorithm Based on B-TREE with Visualization Output

Hierarchical clustering is an unsupervised learning method based on distance metrics that constructs tree-like cluster structures through iterative merging or splitting of data points. The B-TREE, as a self-balancing tree structure, enhances the hierarchical clustering implementation by providing efficient search and insertion operations, thereby optimizing the clustering process. In MATLAB code implementation, this typically involves using balanced tree operations to maintain O(log n) complexity during cluster updates.

Implementing B-TREE-based hierarchical clustering in MATLAB generally follows these key steps: First, compute the distance matrix between data points to establish initial clusters using functions like pdist or squareform. Then, manage the cluster merging/splitting process through B-TREE structure operations, ensuring efficiency through the tree's balancing properties. The algorithm maintains favorable time complexity even with large datasets due to the B-TREE's logarithmic height. Code implementation would involve creating custom B-TREE node classes with cluster information and implementing iterative merging algorithms.

After clustering completion, visualization output becomes crucial for intuitive result interpretation. MATLAB offers comprehensive plotting functions including dendrogram for tree diagrams or scatter plots with cluster differentiation through colors/markers. Through parameter adjustments like node styling (using LineWidth and MarkerSize properties), colormap customization (colormap function), and label annotations (xlabel, ylabel, text functions), visualization quality can be enhanced to provide clearer insights into cluster structures. The dendrogram function specifically allows customization of orientation, color thresholds, and leaf ordering.

This B-TREE-enhanced hierarchical clustering approach not only improves computational efficiency but also effectively supports multidimensional data analysis, making it applicable across various domains including bioinformatics (for gene expression data), market segmentation (customer grouping), and image segmentation (pixel clustering). The implementation can be extended with additional features like different linkage methods (single, complete, average) and distance metrics (Euclidean, Manhattan) specified in the linkage function parameters.