Analysis of retail transactions using gaussian mixture models in a data mining system
View Patent ↗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.
1. A method for analyzing data in a computer-implemented data mining system, comprising:
establishing a database for storing and organizing transactional data, said transactional data being organized within said database in accordance with a data model, said data model comprising a basket table that contains summary information about transactions, an item table that contains information about individual items purchased by customers, and a department table that contains aggregate information about transaction sales by store department; and
mapping the transactional data contained in said database to aggregate the transactional data for cluster analysis.
2. The method of claim 1 , further comprising the step of:
analyzing the transactional data using cluster analysis into coherent groups according to perceived similarities in the transactional data.
3. The method of claim 2 , wherein said analyzing step utilizes a Gaussian Mixture Model.
4. The method of claim 1 , wherein said mapping step aggregates the transactional data into a single flat table view, and each row within said table view includes information concerning a single customer transaction.
5. The method of claim 4 , further comprising the step of:
analyzing the transactional data using cluster analysis into coherent groups according to perceived similarities in the transactional data.
6. The method of claim 5 , wherein said analyzing step utilizes a Gaussian Mixture Model.
7. A computer-implemented data mining system for analyzing data, comprising:
a computerized database for storing and organizing transactional data, said transactional data being organized within said database in accordance with a data model, said data model comprising a basket table that contains summary information about transactions, an item table that contains information about individual items purchased by customers, and a department table that contains aggregate information about transaction sales by store department; and
means for mapping the transactional data contained in said database to aggregate the transactional data for cluster analysis.
8. The system of claim 7 , further comprising:
a cluster analysis program for grouping the transactional data into coherent groups according to perceived similarities in the transactional data.
9. The system of claim 8 , wherein said cluster analysis program utilizes a Gaussian Mixture Model.
10. The system of claim 7 , wherein said means for mapping aggregates the transactional data into a single flat table view, wherein each row within said table view includes information concerning a single customer transaction.
11. The system of claim 10 , further comprising:
a cluster analysis program for grouping the transactional data into coherent groups according to perceived similarities in the transactional data.
12. The system of claim 11 , wherein said cluster analysis program utilizes a Gaussian Mixture Model.