Adaptive Recommendation-based Modeling Support for Data Analysis Workflows

Abstract

RapidMiner is a software framework for the development and execution of data analysis workflows. Like many modern software development environments, the tool comprises a visual editor which allows the user to design processes on a conceptual level, thereby abstracts technical details, and thus helps the user focus on the core modeling task. The large set of pre-implemented data analysis operations available in the framework, as well as their logical dependencies, can, however, be overwhelming in particular for novice users.In this work we present an intelligent add-on to the RapidMiner framework that supports the user during the modeling phase by recommending additional operations to insert into the currently developed data analysis workflow. In the paper, we first propose different recommendation techniques and evaluate them in an offline setting using a pool of several thousand existing workflows. Second, we present the results of a laboratory study, which show that our tool helps users to significantly increase the efficiency of the modeling process.

Performance Evaluation of Sequential Rule Mining Algorithms

“…Mining the sequential rules is an alternative to sequential patterns that assist the users in having knowledge of sequence items for making a prediction. It has been found in numerous zones like electronic learning [5], manufacturing simulation [6], the analysis of customer behavior [7], and decision systems [8].…”