A MULTIDIMENSIONAL RECURSIVE LEARNING PROCESS AND SYSTEM USED TO DISCOVER COMPLEX DYADIC OR MULTIPLE COUNTERPARTY RELATIONSHIPS

摘要

A multidimensional recursive and self-perfecting process used to discover dyatic or multi-counterparty relationships between parties, the process comprising: (a) collecting information from a plurality of data sources; (b) discovering dyatic or multi-counterparty relationships between the parties from the collected information; (c) clustering the parties to infer the dyatic or multi-counterparty relationships between the parties based on common or partially intersecting attributes between the parties, thereby forming clustered parties; (d) evaluating the clustered parties for business linkage potential by integrating the collected information and contextually assessing indicia from the data sources to detect and measure consistency and inconsistency for a given party or dyatic or multi-counterparty relationship; (e) positing and evaluating relationship type and role said party plays in each relationship; and (f) assessing the confidence level regarding the likelihood that the dyatic or multi-counterparty relationship exists between the parties.