Một phương pháp phân cụm bán giám sát mờ đồng huấn luyện trên dữ liệu đa khung nhìn
A Semi-Supervised Fuzzy Clustering Co-Training Approach on Multi-View Data
Abstract
In today’s practical reality, multi-view data is increasingly prevalent. Multi-view data refers to a type of data that encompasses multiple perspectives or viewpoints of an object. Data within each individual view possesses specific attributes dedicated to knowledge discovery and provides information on the same subject with varying degrees of accuracy and reliability. Combining various types of information from different views can yield a more comprehensive and accurate representation of objects, thereby improving data analysis and decision-making. Multi-view clustering has emerged as a research direction that has garnered the interest of scientists in recent years. However, there has been no research focusing on semi-supervised fuzzy clustering combined with co-training algorithms to assess the accuracy and quality of clustering on multi-view datasets. This paper proposes a novel method in semi-supervised clustering, utilizing co-training algorithms on multi-view data collected from a data source. Additionally, the paper provides experimental results to evaluate the effectiveness and accuracy of the proposed algorithm.
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