Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Gaussian random number generators
ACM Computing Surveys (CSUR)
Generating Synthetic Data to Match Data Mining Patterns
IEEE Internet Computing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Learning decision rules from data streams
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Hi-index | 0.00 |
In this paper we introduce a modular, highly flexible, open-source environment for data generation. Using an existing graphical data flow tool, the user can combine various types of modules for numeric and categorical data generators. Additional functionality is added via the data processing framework in which the generator modules are embedded. The resulting data flows can be used to document, deploy, and reuse the resulting data generators. We describe the overall environment and individual modules and demonstrate how they can be used for the generation of a sample, complex customer/product database with corresponding shopping basket data, including various artifacts and outliers.