MATLAB Implementation of Dynamic Clustering Algorithm

Dynamic clustering algorithms play a crucial role in data mining and pattern recognition, automatically adjusting clustering results based on data distribution characteristics. MATLAB, as a powerful scientific computing tool, is well-suited for implementing such algorithms and supports intuitive visualization outputs. ### Algorithm Approach The core of dynamic clustering (such as K-means variants or incremental clustering) lies in real-time adjustment of cluster centers and category divisions. Typical implementation steps include: Initialization: Select initial cluster centers using random selection or heuristic-based methods. Iterative Update: Assign data points to the nearest cluster centers using distance calculations. Recalculate center positions based on newly assigned points using centroid computation. Termination Condition: Stop when cluster center changes fall below a threshold or maximum iterations are reached. Dynamic versions may additionally handle batch updates from data streams or dynamically adjust the number of clusters. ### MATLAB Implementation Key Points Data Preprocessing: Normalize or standardize data using functions like zscore or normalize to improve clustering effectiveness. Distance Calculation: Employ Euclidean distance or cosine similarity, efficiently computed using MATLAB's pdist function with specified distance metrics. Visualization: Utilize scatter function to create point distribution plots, differentiate clusters using distinct colors/markers, and annotate center points with text labels. ### Extended Considerations Dynamic Cluster Number Adjustment: Optimize K-values using silhouette coefficient evaluation or elbow method implementation. Real-time Data Stream Processing: Handle incremental data through sliding window techniques or decay factor mechanisms. Performance Optimization: Leverage matrix operations to avoid loops, or utilize Parallel Computing Toolbox functions like parfor for acceleration. The final output scatter plot clearly displays cluster boundaries and density distribution, helping users intuitively understand the dynamic evolution of data structures.