Search

Current filters:


Current filters:


Refine By:

Search Results

  • previous
  • 1
  • next
Results 1-5 of 5 (Search time: 0.003 seconds).
Item hits:
  • Article

  • Khai thác dữ liệu lớn đối với giáo dục đại học trong bối cảnh cuộc cách mạng công nghiệp 4.0

    • Authors: Lê, Thị Cẩm Bình (2018)

  • Tốc độ phát triển đột phá của công nghệ số trong những năm gần đây như điện thoại thông minh, điện toán đám mây, Internet vạn vật, mạng xã hội, các dịch vụ online,... đã phát sinh một lượng dữ liệu khổng lồ và đến từ nhiều nguồn khác nhau, chủ yếu là từ các phương tiện truyền thông xã hội như Twitters, Youtube, Facebook; các giao dịch kinh doanh như Amazon, e Bay, giao dịch qua mạng hoặc giao dịch từ các thiết bị di động; các máy móc thu nhận dữ liệu như máy gia tốc hạt lớn của CERN, các thiết bị cảm biến,... Các dữ liệu này thường không có cấu trúc như các tài liệu, ảnh, video, audio, email, dữ liệu trên các trang web,.. Theo thống kê, chúng chiếm khoảng trên 80% các loại dữ liệu hiện nay và không ngừng lớn lên. Lượng dữ liệu khổng lồ đó là nguồn gốc ra đời của khái niệm dữ liệu lớ...
  • Article

  • Model of Optimal Centroids Approach for Multivariate Data Classification

    • Authors: Lê, Thị Cẩm Bình; Pham, Van Nha (2021)

  • Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. 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. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOS general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is...
  • 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 are based on single-objective function and single-source dataIn this paper, we pro- pose a new clus...
  • 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 hard clustering methods. Re- cently, fuzzy co-clustering has proven effec- tive in the many-feature da...
  • 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 model (MPSO) is analyzed so that can be applied into complex application models. Then, Model of Optimal ...
  • previous
  • 1
  • next