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Knowledge Discovery in Learning Management System Using Piecewise Linear Regression

机译：基于分段线性回归的学习管理系统中的知识发现

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摘要

Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database.
机译：数据库技术的最新发展已看到各种各样的数据被存储在庞大的集合中。种类繁多使通用数据库的分析任务成为知识发现中的艰巨任务。一种方法是对大型数据集进行汇总，以使所得的汇总数据集具有可管理的大小。直方图作为大型数据库的摘要/代表对象已受到广泛关注。但是，它遭受了计算和空间复杂性的困扰。在本文中，我们提出了一种将直方图对象转换为分段线性回归（PLR）线对象的想法，并提出与直方图相比，PLR对象的计算量和存储量较少。另一方面，为了进行聚类分析，我们提出了一种距离度量，用于计算PLR线之间的距离。基于在线教育系统LMS的真实数据进行了案例研究。这表明PLR是超大型数据库的强大知识代表。

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