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International Conference on Computational Intelligence, Communications, and Business Analytics

CICBA 2017: Computational Intelligence, Communications, and Business Analytics pp 517–530Cite as

Genetic Algorithm-Based Association Rule Mining Approach Towards Rule Generation of Occupational Accidents

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 776))

Abstract

Occupational accident is a grave issue for any industry. Therefore, proper analysis of accident data should be carried out to find out the accident patterns so that precautionary measures could be undertaken beforehand. Association rule mining (ARM) technique is mostly used in this scenario to find out the association (i.e., rules) causing accidents. But, among the rules generated by ARM, all are not useful. To handle this kind of problem, a new model ARM and genetic algorithm (GA) has been proposed in this study. The model automatically selects the optimal Support and Confidence value to generate useful rules. Out of 1285 data obtained from a steel industry in India, eleven useful rules are generated using this proposed method. The findings from this study have the potential to help the management take the better decisions to mitigate the occurrence of accidents.

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Acknowledgement

The authors are thankful to the safety personnel for their kind support from the collection of data to the final evaluation phase of the project.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Sobhan Sarkar & Jhareswar Maiti

  2. Department of Chemical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Ankit Lohani

Authors
  1. Sobhan Sarkar
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  2. Ankit Lohani
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  3. Jhareswar Maiti
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Corresponding author

Correspondence to Sobhan Sarkar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

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© 2017 Springer Nature Singapore Pte Ltd.

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Sarkar, S., Lohani, A., Maiti, J. (2017). Genetic Algorithm-Based Association Rule Mining Approach Towards Rule Generation of Occupational Accidents. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_40

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  • DOI: https://doi.org/10.1007/978-981-10-6430-2_40

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6429-6

  • Online ISBN: 978-981-10-6430-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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