1. A method for analyzing transaction information, comprising: storing each one of a plurality of transactions in an associative learning memory with an associated cluster identification number; selecting a given one of the transactions for analysis, the given one of the transactions having a specific cluster identification number; using an entity analytics engine to search and obtain a first subplurality of transactions from the associative learning memory that are similar to the given one of the transactions by having a common attribute or entity and assigning each one of the first subplurality of transactions a similarity score; further analyzing each one of the transactions in the first subplurality of transactions to determine if any has a similarity score that meets a criterion indicating an increased degree of similarity with the given one of the transactions, and assigning the specific cluster identification number to those ones of the transactions of the first subplurality that meet the criterion, as a second subplurality of transactions; grouping said ones of the transactions that have been assigned the specific cluster identification number in a cluster with the given one of the transactions; analyzing all of the transactions in the cluster and determining a value for the cluster, the value being indicative of a degree to which it would be beneficial to establish a formalized transaction relationship with an organization involved with at least one of the transactions identified with the specific cluster identification number; and further using the entity analytics engine to re-store ones of the transactions in the cluster in the associative learning memory with a new cluster number that matches the specific identification cluster number of the given one of the transactions.