ASSURANCE-ENABLED LINDE BUZO GRAY (ALBG) DATA CLUSTERING BASED SEGMENTATION

Methods and systems for Assurance-enabled Linde Buzo Gray (ALBG) data clustering is described herein. In an implementation, a user model data from a database available to the processor is obtained. The user model data comprises data elements or users, each of which corresponds to features and feature values associated with the users. These data elements of the user model data are segmented into clusters using our segmentation approach with an initial accuracy criterion parametric value and the output is captured as segment data. The segment data output is checked for initial pareto validity. If successful, iterative segmentation run with incremental accuracy criterion using parameterized value is performed till the segmented clusters are determined valid against pareto validity check. The last successful pareto valid segmented cluster data is considered as the finalized segment output data. For an invalid initial pareto validity check, a segmentation run with a pre-determined accuracy criterion value is done to arrive at the finalized segment output data.