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Data Mining with Association Rules for Scheduling Open Elective Courses Using Optimization Algorithms

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

Abstract

A new course scheduling based on mining for students’ preferences for Open Elective courses is proposed in this paper that makes use of optimization algorithms for automated timetable generation and optimization. The Open Elective courses currently running in an actual university system is used for the experiments. Hard and soft constraints are designed based on the timing and classroom constraints and minimization of clashes between teacher schedules. Two different optimization techniques of Genetic Algorithm (GA) and Simulated Annealing (SA) are utilized for our purpose. The generated timetables are analyzed with respect to the timing efficiency and cost function optimization. The results highlight the efficacy of our approach and the generated course schedules are found at par with the manually compiled timetable running in the university.

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Authors and Affiliations

  1. Department of Information Technology, Delhi Technological University, Bawana Road, Delhi, 110042, India

    Seba Susan & Aparna Bhutani

Authors
  1. Seba Susan
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  2. Aparna Bhutani
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Corresponding author

Correspondence to Seba Susan .

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Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Susan, S., Bhutani, A. (2020). Data Mining with Association Rules for Scheduling Open Elective Courses Using Optimization Algorithms. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_75

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