1. A method for implementation by one or more data processors comprising: creating, by at least one data processor, atomic pair-wise relationships between products; creating, by at least one data processor, higher order structures from said relationships between said products; finding, by at least one data processor, a set of all feasible, locally optimal, high cohesiveness product bundles; defining, by at least one data processor, a bundle-lattice-space of feasible bundles comprising a lower bounding foundation set required to be a subset of every feasible product bundle in lattice-space, an upper bounding candidate set required to be a superset of every feasible product bundle in the lattice-space, a bundleness measure of cohesiveness associated with each feasible product bundle, and a neighborhood function that allows either removal of a non-foundation product from, or addition of a candidate product to, a product bundle to reach a neighboring bundle in the lattice space; and initiating a transaction based on the defined bundle-lattice space of feasible bundles; wherein a locally optimal product bundle in the lattice space is defined as a product bundle whose bundle cohesiveness is higher than all of its neighbors, as defined by the neighborhood function, by a certain factor.
1. A method for implementation by one or more data processors comprising: creating, by at least one data processor, atomic pair-wise relationships between products; creating, by at least one data processor, higher order structures from said relationships between said products; finding, by at least one data processor, a set of all feasible, locally optimal, high cohesiveness product bundles; defining, by at least one data processor, a bundle-lattice-space of feasible bundles comprising a lower bounding foundation set required to be a subset of every feasible product bundle in lattice-space, an upper bounding candidate set required to be a superset of every feasible product bundle in the lattice-space, a bundleness measure of cohesiveness associated with each feasible product bundle, and a neighborhood function that allows either removal of a non-foundation product from, or addition of a candidate product to, a product bundle to reach a neighboring bundle in the lattice space; and initiating a transaction based on the defined bundle-lattice space of feasible bundles; wherein a locally optimal product bundle in the lattice space is defined as a product bundle whose bundle cohesiveness is higher than all of its neighbors, as defined by the neighborhood function, by a certain factor. 6. The method of claim 1 , further comprising: using, by at least one data processor, product bundles in retail business decisions and in advanced analysis of retail data, comprising any of: creating product assortment promotions because they capture the latent intentions of customers; creating cross-sell campaigns to promote the right products to a particular customer based on customer transaction history, wherein decisions about right products are derived from products in product bundles that said customer did not buy, while the customer did buy some of the other products in the bundle; and performing a latent intentions analysis of product bundles, where each logical bundle is treated as a latent intention and a customer's purchase history is treated as a mixture of latent intentions.