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Showing results 1 to 4 of 4
  • 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 Co-clustering Algorithm for Multi-source Data

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

  • The development of information and com- munication technology has motivated multi- source data to become more common and publicly available. Compared to traditional data that describe objects from a single- source, multi-source data is semantically richer, more useful, however many-feature, more uncertain, and complex. Since tra- ditional clustering algorithms cannot han- dle such data, multi-source clustering has become a research hotspot. Most existing multi-source clustering methods are devel- oped from single-source clustering by ex- tending the objective function or building combination models. In fact, the fuzzy clus- tering methods handle the uncertainty data better than the ha...
  • 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...
  • Article

  • Model of optimal centroids approach for multivariate data classification

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

  • Abstract-Particle swarm optimization (PSO) is a population- based stochastic optimization algorithm proposed for the first time by Kennedy et al. in 1994. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO's ideas are simple and easy to understand but PSO is only applied in simple model problems. Until now, the official mathematical model of PSO has not been presented. In this paper, will be re-present as a general mathematical model and apply in the multivariate data classification. First, PSO's the general mathematical mode...