臺灣博碩士論文加值系統

本論文的主旨在於研究並利用智慧局部移動法(Smart Local Moving) 於生醫領域相關研究資源的群集尋找上，我們根據實際的平台資料先定義了一個網路模型，並利用點與點之間估計互信息(Estimating Mutual Information) 作為網路中節點間的邊之權重值。以這個模型我們建立了一個查找特定資源與最相關資源的服務。

此外根據這樣的模型我們利用SLM進行分群，並根據Dunn-Index與資料特性自定義了Coherence Index作為群集大小與數量的判別依據，最後以10-folds Cross-Validation進行實驗得出最佳的分群結果，供未來與相關領域專業人員，並進一步改良之討論依據。

A peculiar thing about biomedical researches is that they generally involve more than related past works and literature. Tools, softwares, databases and even samples are considered. Even though many online services have been developed to collect and share these resources. Still, they are too many for researchers to find desire information. For instance, SciCrunch, as one of largest online resource platform, contains more than 15,000 resources.

In this research, we present the analytic result for biomedical research resources in order to figure out meaningful groups of biomedical resources with community detection. We apply the Smart Local Moving algorithm to detect meaningful communities inside the network of resources.

Chapter 1. Introduction 1
Chapter 2. Related Works 3
2.1 Methods Applied for Scholar Research 3
2.2 Determining Number of Communities 5
2.3 Modularity-based Clustering 6
Chapter 3. Methodology 7
3.1 Problem Definition 7
3.2 Estimated Mutual Information for Edges Weights 7
3.3 Clustering Algorithm 10
Chapter 4. Experiment 13
4.1 Dataset 13
4.2 Metrics 14
4.2.1 Weighted Cross-Cluster Accuracy 15
4.2.2 Inverse-Purity 17
4.2.3 Coherence Index 18
4.3 Result 20
Chapter 5. Conclusion 25
Chapter 6. Reference 27

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