Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A data structure for analyzing data in a computer-implemented data mining system. The data structure is a data model that comprises a Gaussian Mixture Model that stores transactional data. The data model is mapped to aggregate the transactional data for cluster analysis.

Abstract: 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.

Abstract: 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.