Multi-thread Implementation of K-Means Clustering
• I designed a project using C++ to simulate the process of K-Means. This implementation aimed to achieve optimal performance and accuracy in clustering data points.
• Multi-threading Technology: To enhance the speed and efficiency of the clustering algorithm, multi-threading techniques were employed. By leveraging parallel processing capabilities, the project achieved faster execution times by distributing the computational workload across multiple threads.
• Visulization: The project utilized C++ visualization packages, such as matplotlibcpp, to create visual representations of the K-Means clustering process. These visualizations helped in better understanding and analyzing the clustering results.