Clustering Student Data to Characterize Performance Patterns 

Abstract

Over the years the academic records of thousands of students have accumulated in educational institutions and most of these data are available in digital format. Mining these huge volumes of data may gain a deeper insight and can throw some light on planning pedagogical approaches and strategies in the future. We propose to formulate this problem as a data mining task and use k-means clustering and fuzzy c-means clustering algorithms to evolve hidden patterns. 

Authors and Affiliations

Bindiya M Varghese , Jose Tomy J , Unnikrishnan A , Poulose Jacob K

Keywords

Related Articles

A Proposed Methodology on Predicting Visitor’s Behavior based on Web Mining Technique

The evolution of the internet in recent decades enlarge the website's reports with the records of user’s activities and behaviors that registered in the web server which can be created automatically in the web access log...

Forecasting Feature Selection based on Single Exponential Smoothing using Wrapper Method

Feature selection is one way to simplify classification process. The purpose is only the selected features are used for classification process and without decreasing its performance when compared without feature selectio...

Pause Time Optimal Setting for AODV Protocol on RPGM Mobility Model in MANETs

For the last few years, a number of routing protocols have been proposed and implemented for wireless mobile Ad hoc network. The motivation behind this paper is to discover and study the pause time effects on Ad hoc on D...

Insight to Research Progress on Secure Routing in Wireless Ad hoc Network

Wireless Ad hoc Network offers a cost effective communication to the users free from any infrastructural dependencies. It is characterized by decentralized architecture, mobile nodes, dynamic topology, etc. that makes th...

Automating Legal Research through Data Mining

The term legal research generally refers to the process of identifying and retrieving appropriate information necessary to support legal decision-making from past case records. At present, the process is mostly manual, b...

Download PDF file
  • EP ID EP155521
  • DOI -
  • Views 92
  • Downloads 0

How To Cite

Bindiya M Varghese, Jose Tomy J, Unnikrishnan A, Poulose Jacob K (2011). Clustering Student Data to Characterize Performance Patterns . International Journal of Advanced Computer Science & Applications, 2(9), 138-140. https://europub.co.uk./articles/-A-155521