Coverage Pattern Mining
Coverage pattern mining is a data mining technique focused on identifying patterns within a dataset that cover a substantial portion of the data, irrespective of their frequency of occurrence. Unlike traditional frequent pattern mining, which prioritizes patterns with high frequency, coverage pattern mining emphasizes patterns that have wide coverage across the dataset. These patterns are considered significant as they provide insights into the overall characteristics and trends present in the data. where understanding patterns that have broad coverage can inform decision-making processes, optimize operations, and improve overall efficiency and effectiveness.
Applications: Retail, Healthcare, Web Usage, Manufacturing, and Social Network Analysis.