Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios
This work addresses the need for more efficient and flexible tools in knowledge discovery for data mining practitioners, though it appears incremental as it combines existing techniques into a new platform.
The researchers tackled the problem of designing prediction models by developing a hybrid platform called SP-CCADM that integrates Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM), and it provided better results than standalone techniques when tested on real-life scenarios with various machine learning algorithms.
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.