S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

•S3Mining framework for supporting novice data miners is proposed.•Model-driven engineering and scientific workflow standards are used by S3Mining framework.•Know-how of expert data miners is used to recommend novice data miners which algorithms to apply.•Meta-data (meta-features) is used to better understand the behavior of data mining algorithms.•S3Mining framework is implemented and available online.•An experimental evaluation is conducted using data sources from the educational domain and also from UCI Machine Learning Repository. Data mining has proven to be very useful in order to extract information from data in many different contexts. However, due to the complexity of data mining techniques, it is required the know-how of an expert in this field to select and use them. Actually, adequately applying data mining is out of the reach of novice users which have expertise in their area of work, but lack skills to employ these techniques. In this paper, we use both model-driven engineering and scientific workflow standards and tools in order to develop named S3Mining framework, which supports novice users in the process of selecting the data mining classification algorithm that better fits with their data and goal. To this aim, this selection process uses the past experiences of expert data miners with the application of classification techniques over their own datasets. The contributions of our S3Mining framework are as follows: (i) an approach to create a knowledge base which stores the past experiences of experts users, (ii) a process that provides the expert users with utilities for the construction of classifiers’ recommenders based on the existing knowledge base, (iii) a system that allows novice data miners to use these recommenders for discovering the classifiers that better fit for solving their problem at hand, and (iv) a public implementation of the framework’s workflows. Finally, an experimental evaluation has been conducted to shown the feasibility of our framework.