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After You, Who? Data Mining for Predicting Replacements

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Mining Intelligence and Knowledge Exploration (MIKE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9468))

Abstract

This paper proposes a new class of data mining problems in which agents replace their current object (predecessor) by another object (replacement or successor); the problem is to discover the knowledge used by the agents in identifying suitable successors. While such replacement data is available in many practical applications, in this paper we explore a problem in HR analytics, viz., replacing person in a key position in a project by another most suitable person from other employees. We propose unsupervised (distance-based) algorithms for finding suitable replacements. We also apply several standard classification techniques. This paper is the first in applying metric learning algorithms to a problem in HR analytics. We compare the approaches using a real-life replacement dataset from a multinational IT company. Results show that metric learning is a promising approach that captures the implicit knowledge for replacement identification.

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Acknowledgments

We thank Dr. Ritu Anand, VP (HR), Preeti Gulati and other HR executives in TCS for enthusiastically supporting this work. Thanks to our team members for discussions and other help.

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Authors and Affiliations

  1. TCS Innovation Labs - TRDDC, Tata Consultancy Services Limited, 54B Hadapsar Industrial Estate, Pune, 411013, India

    Girish Keshav Palshikar, Kuleshwar Sahu & Rajiv Srivastava

Authors
  1. Girish Keshav Palshikar
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  2. Kuleshwar Sahu
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  3. Rajiv Srivastava
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Corresponding author

Correspondence to Girish Keshav Palshikar .

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Editors and Affiliations

  1. Norwegian Univ. of Science & Technology, Trondheim, Norway

    Rajendra Prasath

  2. Intl Inst of Info Tech Hyderabad, Hyderabad, India

    Anil Kumar Vuppala

  3. V.H.N.S.N.College (Autonomous), Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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© 2015 Springer International Publishing Switzerland

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Palshikar, G.K., Sahu, K., Srivastava, R. (2015). After You, Who? Data Mining for Predicting Replacements. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_51

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  • DOI: https://doi.org/10.1007/978-3-319-26832-3_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26831-6

  • Online ISBN: 978-3-319-26832-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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