Research on Network Data Clustering Algorithms in User Group Partitioning

This research delves deeply into the application of network data clustering algorithms in user group partitioning. It elaborates in detail on the characteristics of network data such as complexity and high dimensionality, as well as the crucial role that user group partitioning plays in precision marketing and personalized services. It focuses on analyzing the principles and logics of classic clustering algorithms like K-Means and DBSCAN. K-Means optimizes clustering by iteratively calculating centroids, while DBSCAN partitions groups based on the density regions of data points. Through experimental comparisons, it has been found that when dealing with large-scale and sparse network data, the K-Means clustering algorithm can significantly enhance the clustering effect and efficiency, partition user groups more precisely, and effectively identify user groups with different characteristics, such as highly active user groups and potential consumer user groups, etc. It provides powerful support for enterprises to formulate marketing strategies and optimize services, and thus has important practical value and theoretical significance.