應用視覺化關連性規則於醫學資料探勘—以嚴重急性呼吸道症候群
的文章擷取資訊為例
Applying Visualizing Association Rules in Medical Data Mining —An Example of Information Extraction from Severe Acute Respiratory
中文摘要
本研究，我們應用視覺化的技術: Direct Graph 和 Hyperbolic Tree 於文章的資訊擷 取，並建構了一個整理文獻及呈現文件中詞與文件關連性規則的工具，此系統提 供了使用者一種直覺的方式去挑選文件來節省文件閱讀和實驗設計的時間。最 後，我們使用探討關於嚴重急性呼吸道症候群的文章來呈現本程式從文章中資訊 的擷取和視覺化呈現的幫助。
英文摘要
In this research, we apply the visualization techniques: Direct Graph and Hyperbolic Tree in literature extraction. We implement the tool for researchers to rearrange the literatures and represent the association rules for each term and the documents. It provides users an intuitive way to filter the documents for saving time on paper review and experiment design. In the end, we use the documents which mentioned Severe Acute Respiratory Syndrome (SARS) to demo the literatures extraction and how visualization helps.
