Ấn phẩm:

Fuzzy Co-clustering Algorithm for Multi-source Data

Xem mô tả

0

Xem & Tải

0

Tóm tắt
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 data processing due to the possibility of isolating the uncertainty present in each feature. In this paper, a novel multi-source data mining algorithm based on a modified fuzzy co-clustering algorithm and two penalty terms is proposed, which is called Multi-source Fuzzy Co-clustering Algorithm (MSFCOC)Experimental results on various multi-source datasets indicate that the proposed MSFCOC algorithm outper- forms existing state-of-the-art clustering al- gorithms. Keywords: Data mining, multi-source, fuzzy co-clustering, multi-view, multi- subspace.
Mô tả
Năm xuất bản
2021
Tác giả
Lê, Thị Cẩm Bình
Phạm, Văn Nha
Phạm, Thế Long
Nhà xuất bản
Vui lòng sử dụng ứng dụng TMU DRM để tải/mượn tài liệu số

Thực thể liên kết

Kết quả tìm kiếm tác giả/Nhà nghiên cứu

Tìm kiếm của bạn không trả về kết quả. Bạn có gặp khó khăn khi thực hiện tìm kiếm? Hãy thử lại bằng cách đặt từ khóa tìm vào trong cặp dấu ngoặc kép