Abstract
Clustering is a widely used Data Mining method that aims to partition a given dataset into homogenous groups according to some predefined similarity criterion. The k-modes is a well known categorical clustering method that uses the notion of a mode to represent the centroid in a partition during the clustering process. The mode is a vector containing the most frequent modalities for each attribute. However, in its original version, the mode is selected randomly in each iteration, although many other candidate modes can be proposed. In this paper, a new approach is developed aiming to generate potentially candidate modes for each cluster in each iteration using their relative density. The obtained modes will then be arranged into upper and lower approximation of the Rough Set Theory in order to identify the most pertinent ones. The effectiveness of the proposed method was tested using two real world datasets and compared to the standard k-modes and it was experimentally demonstrated that it provided higher accuracy.
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Ben Salem, S., Naouali, S., Chtourou, Z. (2019). Rough Mode: A Generalized Centroid Proposal for Clustering Categorical Data Using the Rough Set Theory. In: Farhaoui, Y., Moussaid, L. (eds) Big Data and Smart Digital Environment. ICBDSDE 2018. Studies in Big Data, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-030-12048-1_24
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DOI: https://doi.org/10.1007/978-3-030-12048-1_24
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