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

TL;DR

The paper introduces SP-CCADM, a hybrid platform integrating Context-Aware and Collaborative Data Mining techniques, enabling flexible scenario design and testing for improved prediction models validated on real-world data.

Contribution

It presents a novel hybrid platform that combines CADM and CDM, allowing simultaneous scenario configuration and testing, with validation across multiple machine learning algorithms.

Findings

SP-CCADM outperforms standalone CADM and CDM techniques.

The platform effectively handles complex data and contexts.

Validation with various algorithms demonstrates versatility.

Abstract

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.