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11	模糊統計分類及其在茶葉品質評定的應用 / Analysis fuzzy statistical cluster and its application in tea quality 林雅慧, Lin, Ya-Hui Unknown Date (has links) 模糊理論開始於 1960 年代中期,關於這方面的研究與發展均已獲得相當不錯的成果.其中尤以在群落分析應用上的專題研究更是廣泛.Bezdek 提出的模糊分類演算法,乃根據 Dunn 的C平均法所作的一改良方法.但仍有其缺點,例如,未考慮權重且以靜態資料為主. 有鑑於此,本研究對 Bezdek 之方法加以改進推廣,提出加權模糊分類法.對於評價因素為多變量時,應加入模糊權重的考量.此外更結合時間因素,使準則函數成為動態的模式,將傳統的模糊分類法由靜態資料轉為動態資料形式,以反映真實的情況. / Research on the theory of fuzzy sets has been growing steadily since itsinception during the mid-1960s. The literature especially dealing with fuzzycluster analysis is quite extensive. But the research on FCM still has somedisadvantages. For instance, the 群落分析隸屬度函數模糊評鑑表模糊權重加權模糊分類法 Cluster analysis Membership function Fuzzy judgement table Fuzzy weight Weighted fuzzy clustering method
12	EXPLORING GRAPH NEURAL NETWORKS FOR CLUSTERING AND CLASSIFICATION Fattah Muhammad Tahabi (14160375) 03 February 2023 (has links) <p><strong>Graph Neural Networks</strong> (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - <strong>clustering and classification</strong>. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.</p> Biomechanical engineering Neural engineering Health promotion Preventative health care Applications in health Spatial data and applications Evolutionary computation Natural language processing Planning and decision making Data engineering and data science Data mining and knowledge discovery Graph, social and multimedia data Information retrieval and web search Knowledge and information management Context learning Deep learning Neural networks Semi- and unsupervised learning Data structures and algorithms Graph neural network Node classification Graph clustering Temporal graphs dynamic graphs NODE2VEC Graph Attention Mechanism Hunting BiLSTM model EHR data colorectal Cancer Cancers Cancer symptoms symptom Symptom cluster studies Coauthorship networks network analysis Word2vec Hierarchical Clustering method Dunn index semantic analysis text mining Natural Language Processing Tool UMLS identifiers umls Clinical Data Management

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模糊統計分類及其在茶葉品質評定的應用 / Analysis fuzzy statistical cluster and its application in tea quality

EXPLORING GRAPH NEURAL NETWORKS FOR CLUSTERING AND CLASSIFICATION