System and method for association of object sets

A method of determining a preferred grouping scheme, comprising:

• (A) modifying a first original object set to yield a first modified object set, wherein the first original object set and the first modified object set are of different cardinalities;
• (B) modifying a second original object set to yield a second modified object set;
• (C) ordering the first and second modified object sets to yield respective first and second ordered modified object sets;
• (D) calculating a value of a metric taken at least on the first and second ordered modified object sets;
• and
• (E) repeating any of (C) and (D) based at least on the value of the metric.

A method of determining a preferred grouping scheme, comprising:

• (A) modifying a first original object set to yield a first modified object set, wherein the first original object set and the first modified object set are of different cardinality;
• (B) modifying a second original object set to yield a second modified object set;
• (C) calculating a value of a metric taken at least on the first and second modified object sets;
• and
• (D) repeating (A), (B) and (C) until a stopping criterion is satisfied.

A method of determining a preferred grouping scheme, comprising:

• (A) modifying a first original object set to yield a first modified object set;
• (B) modifying a second original object set to yield a second modified object set;
• (C) calculating a value of a metric given by a non-commutative function of at least the first and second modified object sets;
• and
• (D) repeating any of (A) and (B) based at least on the value of the metric.

A method of obtaining a preferred ordering of a first and a second object set, the first and second object sets having a cross-space defining a matrix H, the method comprising:

• (A) choosing a first initial permutation, P1, corresponding to an ordering of the first object set;
• (B) solving a first linear program, max[ƒ(P1, H, P2)]=G1, for a second permutation, P2, corresponding to an ordering of the second object set while keeping P1 fixed;
• (C) solving a second linear program, max[ƒ(P1, H, P2)]=G2, for P1 while keeping P2 fixed;
• and
• (D) repeating (B) and (C) until any of G1 and G2 satisfies a predetermined convergence criterion.

A storage medium on which are coded instruction, which when executed on a data processing system cause the data processing system to:

• (A) modify a first original object set to yield a first modified object set, wherein the first original object set and the first modified object set have different cardinalities;
• (B) modify a second original object set to yield a second modified object set;
• (C) calculate a value of a metric taken at least on the first and second modified object sets;
• and
• (D) repeat any of (A) and (B) based at least on the value of the metric.

A system that performs associations on object sets, comprising:

• a modifier that modifies a first original object set and a second object set to yield a first modified object set and a second modified object set;
• a calculator that calculates a value of a metric taken on at least the first and second modified object sets;
• and
• means for determining whether a function of the value of the metric is consistent.

A method of associating objects, comprising:

• (A) associating at least one element of a first original object set with at least one element of a second original object set;
• (B) modifying elements of the first original object set, producing thereby a first modified object set;
• (C) modifying elements of the second original object set, producing thereby a second modified object set;
• and
• (D) associating at least one element of the first modified object set with at least one element of the second modified object set.

A method of associating objects, comprising:

• (A) segmenting a first object set into a first plurality of object subsets;
• (B) segmenting a second object set into a second plurality of object subsets;
• (C) associating at least one element of the first plurality of object subsets with at least one element of the second plurality of object subsets using an association operation;
• and
• (D) checking whether the association operation is consistent.

A method of associating live data, collected by a data processing system, the method comprising:

• (A) sequentially receiving the live data in discrete packets;
• (B) placing the live data from (A) into at least one dynamic data set;
• (C) augmenting the at least one dynamic data set with new live data as the new live data is received according to (A);
• (D) forming at least two categorical data sets from the elements of the at least one dynamic data set;
• (E) segmenting a first categorical data set into a first plurality of categorical data subsets;
• (F) segmenting a second categorical data set into a second plurality of categorical data subsets;
• and
• (G) associating at least one element of the first plurality of categorical data subsets with at least one element of the second plurality of categorical data subsets using an association operation.

A method for approximating a tendency distribution corresponding to raw data from a plurality of object sets, comprising:

• (A) preconditioning the raw data into a form suitable for association;
• (B) segmenting the raw data into at least two fine-level subsets;
• (C) performing a first association operation between the at least two fine-level subsets;
• (D) aggregating the fine-level subsets to coarse-level subsets corresponding to the fine-level subsets;
• (E) performing a second association operation between the at least two coarse-level subsets;
• and
• (F) comparing results from the first association operation and the second association operations.

An information filtering system comprising:

• a profile subsystem for defining a space of profile data with a particular taxonomy, and for identifying users into a particular partition or category of this predefined taxonomy;
• a manipulatable collaboration subsystem, based on feedback of site usage and a popular decision rule, for identifying a particular suite of content data and delivery scheme to associate with each partition in the profile taxonomy;
• a content delivery subsystem for delivering particular content in combinations, sequences, and schemes as decided by the collaboration subsystem;
• and
• a visualization and analysis subsystem for engaging projections of the collaboration subsystem by either profile-based category or content-based scheme, including category and content indicators indicating other profiles or content that is similar to the object of analysis.

A method for conducting electronic commerce, comprising:

• (A) segmenting a customer base into a plurality of customer segments based on a set of customer attributes;
• (B) segmenting a product base into a plurality of product segments based on a set of product attributes;
• (C) matching a customer segment and a product segment based on a plurality of commercial activity events;
• (D) creating a matrix of customer segments and product segments containing information from joint correlation operations;
• and
• (E) providing the information in the matrix in a manner usable for making marketing decisions in an electronic commerce system.

A method for deriving correspondence between two interactive data sets, comprising:

• (A) dividing a first data set into a plurality of first data set segments;
• (B) dividing a second data set into a plurality of second data set segments;
• (C) evaluating a joint distribution matrix to determine a relevance indicator for indicating relative relevance of the first and second data set segments to one another;
• (D) subdividing the first and second data set segments into finer segments and performing act (C) on the finer segments;
• (E) aggregating the data set segments into coarser data set representations having fewer segments if the relevance indicator indicates a lack of relevance between the first and second data set segments;
• and
• (F) exiting the process when the relevance indicator meets a preset condition.