Abstract
Big data clustering has become an important challenge in data mining. Indeed, Big data are often characterized by a huge volume and a variety of attributes namely, numerical and categorical. To deal with these challenges, we propose the parallel k-prototypes method which is based on the Map-Reduce model. This method is able to perform efficient groupings on large-scale and mixed type of data. Experiments realized on huge data sets show the performance of the proposed method in clustering large-scale of mixed data.
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HajKacem, M.A.B., N’cir, CE.B., Essoussi, N. (2015). Parallel K-prototypes for Clustering Big Data. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_61
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DOI: https://doi.org/10.1007/978-3-319-24306-1_61
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