论文标题
设计混合协作和上下文感知数据挖掘方案的创新平台
Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios
论文作者
论文摘要
如今，知识发现的过程涉及大量技术。上下文感知数据挖掘（CADM）和协作数据挖掘（CDM）是最近的一些。当前的研究提出了一种新的混合和高效工具，用于设计称为“方案平台 - 合理和上下文感知数据挖掘（SP-CCADM）”的预测模型。新平台以灵活的方式包含了CADM和CDM方法。 SP-CCADM允许一次设置和测试与数据挖掘有关的多个可配置方案。在现实生活中，已成功测试和验证了引入的平台，比每种独立技术cadm和CDM提供了更好的结果。然而，SP-CCADM通过各种机器学习算法-K-Nearest邻居（K-NN），深度学习（DL），梯度增压树（GBT）和决策树（DT）验证。 SP-CCADM在面对复杂数据，正确接近数据上下文和数据之间的协作时向前迈出了一步。数值实验和统计数据详细说明了所提出的平台的潜力。
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.
