Abstract
This research presents the knowledge discovery using Data Mining from the organization and with a KPI management point of view. The stages presented here are based on techniques and Data Mining models, with emphasis on clustering techniques, such as the C-MEANS algorithm. We both consider the classic and fuzzy perspectives, namely Fuzzy C-MEANS and K-MEANS, and then compare the results based on the level of support which each algorithm provides to information management. The CRISP-DM methodology is used in our implementation, which is then applied to three case studies.
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Urrutia, A., Valdes, H., Galindo, J. (2013). Comparison of K-Means and Fuzzy C-Means Data Mining Algorithms for Analysis of Management Information: An Open Source Case. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_23
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DOI: https://doi.org/10.1007/978-3-319-00551-5_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00550-8
Online ISBN: 978-3-319-00551-5
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