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Stakeholders Classification System Based on Clustering Techniques

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Advances in Artificial Intelligence - IBERAMIA 2018 (IBERAMIA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11238))

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Abstract

Stakeholder classification is carried out by project managers using methods such as interviews with experts, brainstorming and checklists. These methods are carried out manually and present a subjective character as they depend on the appreciation of the interviewees. It affects the accuracy of the classification and the making-decisions. The objective of this research is to propose a fuzzy inference system for the classification of stakeholders, which will improve the quality of such classification in the projects. The proposal performs the automatic learning and the adjustment of the fuzzy inference system to classify the stakeholders executing two clustering algorithms: SBC and DENFIS. It examines the results of applying them in 10 iterations by calculating the measures: accuracy, false positive cases, false negative cases, mean square error and symmetric mean absolute percentage error. The best results are shown by the SBC algorithm. The fuzzy inference system for stakeholder’s classification generated improves the quality of this classification as well as the tools to support decision-making in organizations oriented to projects.

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Notes

  1. 1.

    Repository of packages with algorithms implemented in R for its application in different domains. Available in: http://cran.r-project.org/web/packages/.

  2. 2.

    Systems based on fuzzy rules for classification and regression tasks. Available in: https://cran.r-project.org/web/packages/frbs/.

References

  1. The Standish Group: Standish Group 2015 Chaos Report - Q&A with Jennifer Lynch. InfoQ (2015)

    Google Scholar 

  2. Pico, Ó.: Los stakeholders como actores estratégico-instrumentales en los proyectos de la Nueva Gestión Pública. Tesis de maestría, Universidad de Oviedo, España (2016). http://digibuo.uniovi.es/dspace/bitstream/10651/38421/4/TFMOscarPicoRUO.pdf

  3. Schwalbe, K.: Information Technology Project Management. Cengage Learning, New York (2015)

    Google Scholar 

  4. PMI: A guide to the project management body of knowledge, 5th edn. Project Management Institute Inc., Pennsylvania (2013). ISBN 978-1-935589-67-9

    Google Scholar 

  5. Bello, R., Verdegay, J.L.: Los conjuntos aproximados en el contexto de la soft computing. Revista Cubana de Ciencias Informáticas 4(1–2) (2011). http://rcci.uci.cu/?journal=rcci&page=article&op=view&path%5B%5D=103

  6. Piñero, P.Y., García, M.M.: Un modelo para el aprendizaje y la clasificación automática basado en técnicas de softcomputing. Tesis de doctorado. Universidad Central de las Villas, Santa Clara (2005)

    Google Scholar 

  7. Kanungo, T., et al.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002). https://www.cs.umd.edu/~mount/Projects/KMeans/pami02.pdf, https://doi.org/10.1109/TPAMI.2002.1017616

    Article  Google Scholar 

  8. Mainardes, E.W., Alves, H., Raposo, M.: A model for stakeholder classification and stakeholder relationships. Manag. Decis. 50(10), 1861–1879 (2012). https://doi.org/10.1108/00251741211279648

    Article  Google Scholar 

  9. Mitchell, R.K., Agle, B.R., Wood, D.J.: Toward a theory of stakeholder identification and salience: defining the principle of who and what really counts. Acad. Manag. Rev. 22(4), 853 (1997). https://doi.org/10.5465/AMR.1997.9711022105

    Article  Google Scholar 

  10. Poplawska, J., Labib, A., Reed, D.M., Ishizaka, A.: Stakeholder profile definition and salience measurement with fuzzy logic and visual analytics applied to corporate social responsibility case study. J. Clean. Prod. 105, 103–115 (2015). https://doi.org/10.1016/j.jclepro.2014.10.095

    Article  Google Scholar 

  11. Samboni, A.P., Blanco, J.G.: Herramientas de gestión de interesados utilizadas en las etapas de planeación y control de proyectos. Tesis de especialidad, Universidad de San Buenaventura, Santiago de Cali (2015). http://bibliotecadigital.usb.edu.co/bitstream/10819/2549/1/Herramientas_Interesados_Gestion_Etapas_Control_Proyectos_Samboni_2015.pdf

  12. Arévalo, A.U., Requena, R.: Considerations of the stakeholder approach. Punto de Vista 4(7), 31–50 (2013)

    Google Scholar 

  13. Bernal, A., Rivas, L.A.: Modelos para la identificación de stakeholders y su aplicación a la gestión de los pequeños abastecimientos comunitarios de agua. Revista Lebret 4(4), 251–273 (2012). https://doi.org/10.15332/rl.v4i4.337

    Article  Google Scholar 

  14. Acuña, A.P.: La gestión de los stakeholders. Análisis de los diferentes modelos. Grupo de Investigación RSE y Sistemas de Información de la Universidad Nacional del Sur, pp. 1–12 (2012). http://www.adenag.org.ar/uploads/congresos/regionales/Ponencia_Acu%C3%B1a.pdf

  15. Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Commun. ACM 37(3), 77–85 (1994). https://doi.org/10.1145/175247.175255

    Article  Google Scholar 

  16. Mamdani, E.H.: Application of fuzzy logic to approximate reasoning using linguistic synthesis. In: International Symposium on Multiple-valued logic, pp. 196–202. IEEE Computer Society Press (1976). https://doi.org/10.1109/TC.1977.1674779

    Article  Google Scholar 

  17. Sugeno, M.: Fuzzy measure and fuzzy integral. Trans. Soc. Instrum. Control Eng. 8(2), 218–226 (1972). https://doi.org/10.9746/sicetr1965.8.218

    Article  Google Scholar 

  18. Tsukamoto, Y.: An Approach to Fuzzy Reasoning Method. North-Holland Publishing Company, Amsterdam, New York (1979)

    Google Scholar 

  19. Lugo, J.A., Piñero, P.Y., Bello, R., Delgado, R.: Modelo para el control de la ejecución de proyectos basado en soft computing. Tesis de doctorado, Universidad de las Ciencias Informáticas, La Habana (2016). https://doi.org/10.13140/RG.2.1.4270.8565

  20. Lugo, J.A., Delgado, R.: Modelo para el control de la ejecución de proyectos basado en indicadores y lógica borrosa. Tesis de maestría. Universidad de las Ciencias Informáticas, La Habana (2013). https://doi.org/10.13140/2.1.1523.5362

  21. Soria, L., Verdecia, P.: Estimación del esfuerzo en proyectos a partir de puntos de función y técnicas de soft computing. Tesis de maestría. Universidad de las Ciencias Informáticas, La Habana (2015)

    Google Scholar 

  22. Herrera, F.: Genetic fuzzy systems: taxonomy, current research trends and prospects. J. Evol. Intell. 1(1), 27–46 (2008). https://doi.org/10.1007/s12065-007-0001-5. ISSN 1864-5917

    Article  Google Scholar 

  23. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press (1967)

    Google Scholar 

  24. Napook, P., Eiamkanitchat, N.: The adaptive dynamic clustering neuro-fuzzy system for classification. In: Kim, K.J. (ed.) Information Science and Applications. LNEE, vol. 339, pp. 721–728. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46578-3_85

    Chapter  Google Scholar 

  25. Prasad, M., et al.: Collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers. In: 2014 IEEE Symposium on Computational Intelligence in Control and Automation, CICA, Orlando, Florida, pp. 1–6 (2014). https://doi.org/10.1109/CICA.2014.7013227

  26. Chou, K.P., et al.: Takagi-Sugeno-Kang type collaborative fuzzy rule based system. In: IEEE Symposium on Computational Intelligence and Data Mining, CIDM, Orlando, Florida, pp. 315–320 (2014). https://doi.org/10.1109/cidm.2014.7008684

  27. Chiu, S.: Method and software for extracting fuzzy classification rules by subtractive clustering. In: Fuzzy Information Processing Society, NAFIPS, pp. 461–465 (1996). https://doi.org/10.1109/NAFIPS.1996.534778

  28. Yager, R., Filev, D.: Generation of fuzzy rules by mountain clustering. J. Intell. Fuzzy Syst. 2(3), 209–219 (1994). https://doi.org/10.3233/IFS-1994-2301

    Article  Google Scholar 

  29. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984). https://doi.org/10.1016/0098-3004(84)90020-7

    Article  Google Scholar 

  30. Kasabov, N.K., Song, Q.: DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. Fuzzy Syst. 10(2), 144–154 (2002). https://doi.org/10.1109/91.995117

    Article  Google Scholar 

  31. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985). https://doi.org/10.1109/TSMC.1985.6313399

    Article  MATH  Google Scholar 

  32. Song, Q., Kasabov, N.: ECM - a novel on-line evolving clustering method and its applications. In: Posner, M.I. (ed.) Foundations of Cognitive Science, pp. 631–682. The MIT Press, Cambridge (2001)

    Google Scholar 

  33. Riza, L.S., Christoph, B., Herrera, F., Benítez, J.M.: Frbs: fuzzy rule-based systems for classification and regression in R. J. Stat. Softw. 65(6), 130 (2015). https://doi.org/10.18637/jss.v065.i06

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. La Salle University, Arequipa, Peru

    Yasiel Pérez Vera

  2. University of Informatics Science, Havana, Cuba

    Anié Bermudez Peña

Authors
  1. Yasiel Pérez Vera
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Correspondence to Yasiel Pérez Vera .

Editor information

Editors and Affiliations

  1. Universidad Nacional del Sur, Bahía Blanca, Buenos Aires, Argentina

    Guillermo R. Simari

  2. University of Madeira, Funchal, Portugal

    Eduardo Fermé

  3. Universidad Nacional de Piura, Castilla-Piura, Peru

    Flabio Gutiérrez Segura

  4. Universidad Nacional de Trujillo, Trujillo, Peru

    José Antonio Rodríguez Melquiades

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Pérez Vera, Y., Bermudez Peña, A. (2018). Stakeholders Classification System Based on Clustering Techniques. In: Simari, G., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_20

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  • DOI: https://doi.org/10.1007/978-3-030-03928-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03927-1

  • Online ISBN: 978-3-030-03928-8

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