Prognostication of Student’s Performance: An Hierarchical Clustering Strategy for Educational Dataset

  • Parag Bhalchandra
  • Aniket Muley
  • Mahesh Joshi
  • Santosh Khamitkar
  • Nitin Darkunde
  • Sakharam Lokhande
  • Pawan Wasnik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 410)


The emerging field of educational data mining gives us better perspectives for insights in educational data. This is done by extracting hidden patterns in educational databases. In these lines, the objective of this research work is to introduce hierarchical clustering models for student’s collected data. The ultimate goal is to find attributes in terms of set of clusters which severely affect the student’s performance. Here clustering is intentionally used as the most common causes affecting performance within the database which cannot be seen normally. The results enable us to use discovered characteristic or patterns in palpating student’s learning outcomes. These patterns can be useful for teachers to identify effective prognostication strategies for students.


Educational data mining Clustering Performance analysis Pattern discovery 


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Copyright information

© Springer India 2016

Authors and Affiliations

  • Parag Bhalchandra
    • 1
  • Aniket Muley
    • 2
  • Mahesh Joshi
    • 3
  • Santosh Khamitkar
    • 1
  • Nitin Darkunde
    • 2
  • Sakharam Lokhande
    • 1
  • Pawan Wasnik
    • 1
  1. 1.School of Computational SciencesS.R.T.M. UniversityNandedIndia
  2. 2.School of Mathematical SciencesS.R.T.M. UniversityNandedIndia
  3. 3.School of Educational SciencesS.R.T.M. UniversityNandedIndia

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