Abstract
Over the recent decades, there has been an exponential growth of data streaming from various data sources, such as social networks and data centers. As data grows larger, clustering has become a challenging task. This paper proposes a new multi fuzzy ensemble cluster for big data to address this challenge. This approach is based on the use of the data reduction and the cluster forests (CF) strategy (FCFDR). It comprises two tasks. The first consists of making many clustering instances based on hybridizing feature selection (FS) with instance selection (IS), which are used to select more representative features from big data, to simultaneously reduce the high dimensional data. The selected features are then classified using the objective function. Yielding the initial fuzzy co-association matrix and regularized it. The second task, consists of aggregating the clustering instances into one final result vector, using normalized cut algorithm (Ncut). A big datasets from UCI was used to validate the effectiveness of our approach. The results prove the efficiency of the proposed approach in terms of clustering accuracy and quality.
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Lahmar, I., Zaier, A., Yahia, M., Boaullegue, R. (2022). A Multiple Fuzzy C-Means Ensemble Cluster Forest for Big Data. In: Abraham, A., et al. Hybrid Intelligent Systems. HIS 2021. Lecture Notes in Networks and Systems, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-030-96305-7_41
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DOI: https://doi.org/10.1007/978-3-030-96305-7_41
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