论文标题
用于电子学习的多源数据挖掘
Multi-source Data Mining for e-Learning
论文作者
论文摘要
数据挖掘是在大型数据集中发现有趣，出乎意料或有价值的结构的任务，并将其转换为可理解的结构以供进一步使用。已经提出了数据挖掘领域的不同方法，其中最重要的是一种模式挖掘。模式挖掘涉及从数据中提取有趣的频繁模式。模式挖掘已成为一个高度兴趣的话题，例如，它用于不同的目的。该领域中一些最常见的挑战包括降低过程的复杂性并避免模式内的冗余。到目前为止，模式挖掘主要集中在单个数据源的采矿上。但是，随着数据量的增加，在数量，源的多样性和数据性质方面，采矿多源和异质数据已成为该领域的新挑战。这项挑战是我们工作的主要重点，我们建议在其中开采多源数据，以提取有趣的频繁模式。
Data mining is the task of discovering interesting, unexpected or valuable structures in large datasets and transforming them into an understandable structure for further use . Different approaches in the domain of data mining have been proposed, among which pattern mining is the most important one. Pattern mining mining involves extracting interesting frequent patterns from data. Pattern mining has grown to be a topic of high interest where it is used for different purposes, for example, recommendations. Some of the most common challenges in this domain include reducing the complexity of the process and avoiding the redundancy within the patterns. So far, pattern mining has mainly focused on the mining of a single data source. However, with the increase in the amount of data, in terms of volume, diversity of sources and nature of data, mining multi-source and heterogeneous data has become an emerging challenge in this domain. This challenge is the main focus of our work where we propose to mine multi-source data in order to extract interesting frequent patterns.
