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Improving Team Performance by Using Clustering and Features Reduction Techniques

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Proceedings of the 5th International Conference on Big Data and Internet of Things (BDIoT 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 489))

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Abstract

In a global context where competition is increasing, companies are constantly looking for sustainable competitive advantages that will allow them to improve their market shares and profit margins, improving team performance is one of the key factors in this process which allows these organizations to increase their productivity, their competitiveness, and their profitability.

Evaluating this team performance is one of the major challenges of human resources management, which has experienced in recent years a profound digital transformation of data and their management, current IT tools are no longer able to use the mass of data resulting from several sources and which does not stop multiplying from one day to another, or to find correlations between them to draw new knowledge and to anticipate future events.

The purpose of our research is to establish a team classification model according to several performance factors using Machine Learning algorithms, in particular for dimensionality reduction and clustering, The result of this work represents a decision support model for companies to develop a tailor-made team about the overall strategy of the company, to set up an action plan adapted to each team cluster and to anticipate future events, namely departures.

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Acknowledgements

I would like to express my deep gratitude to all who have provided me with the opportunity to complete this report. I would particularly like to thank my advisors Pr. Aknin Noura, Pr Chrayah Mohamed, and Pr. Elkadiri Kamal Eddine whose contribution by stimulating suggestions and encouragement helped me to finalize this work.

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Authors and Affiliations

  1. FS, Abdelmalek Essaadi University, Tetouan, Morocco

    Zbakh Mourad, Aknin Noura & Elkadiri Kamal Eddine

  2. ENSATE, Abdelmalek Essaadi University, Tetouan, Morocco

    Chrayah Mohamed

Authors
  1. Zbakh Mourad
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  2. Aknin Noura
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  3. Chrayah Mohamed
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  4. Elkadiri Kamal Eddine
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Corresponding author

Correspondence to Zbakh Mourad .

Editor information

Editors and Affiliations

  1. ENSIAS, Mohammed V University, Rabat, Morocco

    Mohamed Lazaar

  2. UNILEHAVRE, UNIROUEN, Normandie Université, Le Havre, France

    Claude Duvallet

  3. Vrije Universiteit Brussel, Brussels, Belgium

    Abdellah Touhafi

  4. ENSA, Abdelmalek Essaâdi University, Tetuan, Morocco

    Mohammed Al Achhab

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Mourad, Z., Noura, A., Mohamed, C., Eddine, E.K. (2022). Improving Team Performance by Using Clustering and Features Reduction Techniques. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_26

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