Abstract
Clustering method is one of the most important and basic technique for data mining which aims to group a collection of samples into clusters based on similarity. Clustering Big datasets has always been a serious challenge due to its high dimensionality and complexity. In this paper, we propose a novel clustering algorithm which aims to introduce the concept of intuitionistic fuzzy set theory onto the framework of CLARANS for handling uncertainty in the context of mining Big datasets. We also suggest a new scalable approximation to compute the maximum number of neighbors. Our experimental evaluation on real data sets shows that the proposed algorithm can obtain satisfactory clustering results and outperforms other current methods. The clusters quality was evaluated by three well-known metrics.
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Shili, H., Romdhane, L.B. (2018). IF-CLARANS: Intuitionistic Fuzzy Algorithm for Big Data Clustering. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_4
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DOI: https://doi.org/10.1007/978-3-319-91476-3_4
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