A computer-implemented method for processing multidimensional user data in which each dimension relates to specific attributes or characteristics of the user data, the method comprising: partitioning, into a plurality of first bins, user data for each dimension of a plurality of dimensions, wherein the plurality of dimensions includes a maximum number of dimensions; defining as clusters, the first bins that include the user data for a number of users above a threshold; storing the clusters for each dimension into a cluster definitions table; adding, incrementally, a number of dimensions into a combination of dimensions, and identifying every combination of clusters above the threshold, wherein the number of dimensions in the combination defines the dimensionality of the combination; partitioning, into second bins, each of said combination of clusters; defining, as overlaid clusters, the second bins that include the user data that exceeds the threshold number of users; storing the overlaid clusters in the cluster definitions table; and searching the cluster definitions table for at least one associated cluster in a highest dimension for association with a specific user, wherein the search comprises: omitting clusters that do not contain client cluster criteria and user characteristics for the specific user, and leaving remaining clusters; comparing user characteristics for the specific user to clusters that have the highest level of dimensionality that contains the user characteristics and indicating that the at least one associated cluster is found when user specific data is within the remaining cluster having the highest dimensionality; and decrementing the comparison, of the user specific data down to a cluster having the next highest dimensionality and comparing the user specific data to clusters having the next level of dimensionality wherein the decrementing is repeatedly done until the at least one associated cluster is found. partitioning, into a plurality of first bins, user data for each dimension of a plurality of dimensions, wherein the plurality of dimensions includes a maximum number of dimensions; defining as clusters, the first bins that include the user data for a number of users above a threshold; storing the clusters for each dimension into a cluster definitions table; adding, incrementally, a number of dimensions into a combination of dimensions, and identifying every combination of clusters above the threshold, wherein the number of dimensions in the combination defines the dimensionality of the combination; partitioning, into second bins, each of said combination of clusters; defining, as overlaid clusters, the second bins that include the user data that exceeds the threshold number of users; storing the overlaid clusters in the cluster definitions table; and searching the cluster definitions table for at least one associated cluster in a highest dimension for association with a specific user, wherein the search comprises: omitting clusters that do not contain client cluster criteria and user characteristics for the specific user, and leaving remaining clusters; comparing user characteristics for the specific user to clusters that have the highest level of dimensionality that contains the user characteristics and indicating that the at least one associated cluster is found when user specific data is within the remaining cluster having the highest dimensionality; and decrementing the comparison, of the user specific data down to a cluster having the next highest dimensionality and comparing the user specific data to clusters having the next level of dimensionality wherein the decrementing is repeatedly done until the at least one associated cluster is found. omitting clusters that do not contain client cluster criteria and user characteristics for the specific user, and leaving remaining clusters; comparing user characteristics for the specific user to clusters that have the highest level of dimensionality that contains the user characteristics and indicating that the at least one associated cluster is found when user specific data is within the remaining cluster having the highest dimensionality; and decrementing the comparison, of the user specific data down to a cluster having the next highest dimensionality and comparing the user specific data to clusters having the next level of dimensionality wherein the decrementing is repeatedly done until the at least one associated cluster is found.