OURMINE: An open source data mining toolkit

When researchers want to repeat, improve or refute prior conclusions, it is useful to have a complete and operational description of prior experiments. If those descriptions are overly long or complex, then sharing their details may not be informative.;OURMINE is a scripting environment for the development and deployment of data mining experiments. Using OURMINE, data mining novices can specify and execute intricate experiments, while researchers can publish their complete experimental rig alongside their conclusions.;This is achievable because of OURMINE\u27s succinctness. For example, this thesis presents three case studies documented in the OURMINE syntax. Thus, the brevity and simplicity of OURMINE recommends it as a better tool for documenting, executing, and sharing data mining experiments

How to Build Repeatable Experiments

Associated research group: Critical Systems Research GroupThe mantra of the PROMISE series is "repeatable, improvable, maybe refutable" software engineering experiments. This community has successfully created a library of reusable software engineering data sets. The next challenge in the PROMISE community will be to not only share data, but to share experiments. Our experience with existing data mining environments is that these tools are not suitable for publishing or sharing repeatable experiments. OURMINE is an environment for the development of data mining experiments. OURMINE offers a succinct notation for describing experiments. Adding new tools to OURMINE, in a variety of languages, is a rapid and simple process. This makes it a useful research tool. Complicated graphical interfaces have been eschewed for simple command-line prompts. This simplifies the learning curve for data mining novices. The simplicity also encourages large scale modification and experimentation with the code. In this paper, we show the OURMINE code required to reproduce a recent experiment checking how defect predictors learned from one site apply to another. This is an important result for the PROMISE community since it shows that our shared repository is not just a useful academic resource. Rather, it is a valuable resource industry: companies that lack the local data required to build those predictors can use PROMISE data to build defect predictors