Knowledge Discovery in Learning Management System Using Piecewise Linear Regression - Circuits and Systems

CS > Vol.7 No.11, September 2016

Knowledge Discovery in Learning Management System Using Piecewise Linear Regression ()

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DOI: 10.4236/cs.2016.711322 1,633 Downloads 2,620 Views Citations

Author(s)

S. Mythili¹, R. Pradeep Kumar¹, P. Nagabhushan²

Affiliation(s)

¹Department of Computer Science, United Institute of Technology, Anna University, Chennai, India.
²Department of Computer Science University of Mysore, Mysore, India.

ABSTRACT

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.

KEYWORDS

Histogram, Piecewise Linear Regression, Knowledge Discovery, Big Data, Cluster Analysis

Share and Cite:

Mythili, S. , Kumar, R. and Nagabhushan, P. (2016) Knowledge Discovery in Learning Management System Using Piecewise Linear Regression. Circuits and Systems, 7, 3862-3873. doi: 10.4236/cs.2016.711322.

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