Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios

The process of knowledge discovery involves nowadays a major number of techniques. Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) are some of the recent ones. the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM). Both CADM and CDM approaches are included in the new platform in a flexible manner; SP-CCADM allows the setting and testing of multiple configurable scenarios related to data mining at once. The introduced platform was successfully tested and validated on real life scenarios, providing better results than each standalone technique—CADM and CDM. Nevertheless, SP-CCADM was validated with various machine learning algorithms—k-Nearest Neighbour (k-NN), Deep Learning (DL), Gradient Boosted Trees (GBT) and Decision Trees (DT). SP-CCADM makes a step forward when confronting complex data, properly approaching data contexts and collaboration between data. Numerical experiments and statistics illustrate in detail the potential of the proposed platform.

Survey of Collaborative Data Mining

In the information industry there is a huge amount of data which can be used to obtain relevant information necessary for them to be processed, cleaned and analyzed. This survey paper presents different data extraction research that can be applied in various areas. The main point is to determine a new approach and to be the starting point for new experiments in agriculture predictions. The collaborative data mining approach assumes that correlated units or devices will have similar behavior that can be determined with an acceptable approximation.