Mobile User Cluster Identification via CDR Graph Partitioning

The patent segments the large-scale CDR data processing into distinct functional modules: data collection module, data cleaning module, clustering analysis module, and marketing application module. This segmentation reduces processing complexity by handling different aspects of data analysis separately while maintaining the ability to extract comprehensive business intelligence.

The patent introduces intermediate data structures and processing layers between raw CDR data and final marketing insights. Specifically, it creates user behavior profiles and cluster characteristics as intermediary representations that simplify the complex relationship between raw call records and marketing decisions.

The patent performs preliminary data cleaning and preprocessing operations on CDR records before conducting detailed clustering analysis. By pre-processing the data to remove inconsistencies and standardize formats, the system reduces the computational burden of subsequent analysis while maintaining identification accuracy.

The patent implements a two-stage clustering approach where it first performs a coarse-grained clustering to identify potential user groups, then applies more detailed analysis only to promising clusters. This partial application of detailed analysis reduces overall processing time while maintaining accuracy for the most important clusters.