Ncr Corporation

Scott Woodroofe Cunningham

Paul M Cereghini

Sreedhar Srikant

James Dean Hildreth

Randy G Kerber

Ellen M Boerger

Mikael Bisgaard-B Hr

A computer-implemented data mining system that analyzes data using Gaussian Mixture Models. The data is accessed from a database, and then an Expectation-Maximization (EM) algorithm is performed in the computer-implemented data mining system to create the Gaussian Mixture Model for the accessed data. The EM algorithm generates an output that describes clustering in the data by computing a mixture of probability distributions fitted to the accessed data.

Analysis of retail transactions using gaussian mixture models in a data mining system

The customer segmentation software according to the present invention automatically finds or creates profiles of prototypical customers in a large e-commerce database. The software matches all existing customer data in the database to one or more of the prototypical customers. The resulting customer segmentation is an effective summarization of the database and is useful for a range of business applications. Applications of the customer segmentation system include the development of customized web sites, the creation of targeted promotional offers and the prediction of consumer behavior.

Method for customer segmentation with applications to electronic commerce

A method, apparatus, and article of manufacture for performing data mining applications in a relational database management system. An analytic algorithm for cluster analysis is performed by the computer. The analytic algorithm for cluster analysis includes SQL statements and programmatic iteration for finding one or more groupings in the data retrieved from the relational database management system and for identifying homogenous ones of the groupings as clusters. The analytic algorithm for cluster analysis creates at least one analytic model within an analytic logical data model from data residing in the relational database.

SQL-based analytic algorithm for cluster analysis

A computer-implemented data mining system includes an Interface Tier, an Analysis Tier, and a Database Tier. The Interface Tier supports interaction with users, and includes an On-Line Analytic Processing (OLAP) Client that provides a user interface for generating SQL statements that retrieve data from a database, and an Analysis Client that displays results from a data mining algorithm. The Analysis Tier performs one or more data mining algorithms, and includes an OLAP Server that schedules and prioritizes the SQL statements received from the OLAP Client, an Analytic Server that schedules and invokes the data mining algorithm to analyze the data retrieved from the database, and a Learning Engine performs a Learning step of the data mining algorithm. The Database Tier stores and manages the databases, and includes an Inference Engine that performs an Inference step of the data mining algorithm, a relational database management system (RDBMS) that performs the SQL statements against a Data Mining View to retrieve the data from the database, and a Model Results Table that stores the results of the data mining algorithm.

Architecture for distributed relational data mining systems

A system for optimizing the promotional sale of a product, a product segment, or a category which may take into account related products or competing products comprising means for generating a three-dimensional data structure corresponding to the sales history for a product, the data structure dimensions corresponding to an event type domain, a time domain, and a unit of measurement domain, means for populating the three-dimensional data structure, a neural network, means for training the neural network and means for applying sales objectives and constraints to the neural network.

Growing community of inventors

Mountain View, CA, United States of America

Scott Woodroofe Cunningham