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  • Article

  • An ensemble model approach for many-feature data clustering

    • Authors: Lê, Thị Cẩm Bình; Ngô, Thành Long; Phạm, Văn Nha; Phạm, Thế Long (2021)

  • The ensemble is a popular machine learning technique based on the principle of divide and conquer. In data clustering, the ensemble aims to improve performance in terms of processing speed and clustering quality. Most existing ensemble methods face inherent complex challenges such as uncertainty, ambiguity, and overlap. Fuzzy clustering has recently been developed to handle data with many-feature, heterogeneity, uncertainty, and big data. In this paper, we propose an ensemble feature- reduction clustering model (EFRC) using advanced machine learning techniques. The EFRC model consists of three phases. First, the data is feature-reduced by a random projection. Then, the data is divided...
  • Article

  • Fuzzy optimization multi-objective clustering Ensemble model for multi-source data analysis

    • Authors: Lê, Thị Cẩm Bình; Phạm, Văn Nha; Ngô, Thành Long (2021)

  • In modern data analysis, multi-source data appears more and more in real applications. Different data sources provide information about different data. Therefore, multi-source data linking is important to improve the processing performance. However, in practice multi-source data is often heterogeneous, un- certain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a universal machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of ac- curacy and robustness. However, most of the traditional clustering ensemble approaches...