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題名:最大懲罰概似原則之模糊集群演算法運用於 探勘紅斑性狼瘡患者之中醫潛在證型類別
作者:吳文祥
作者(外文):Wen-Shiang Wu
校院名稱:銘傳大學
系所名稱:管理科學研究所博士班
指導教授:林進財
學位類別:博士
出版日期:2004
主題關鍵詞:決策支援系統模糊集群演算法期望最大化演算法潛在群體分析資料探勘系統性紅斑性狼瘡fuzzy clustering algorithmclinical decision-support systemslatent class modelexpectation maximization (EM) algorithmData miningsystematic lupus erythematosus (SLE)
原始連結:連回原系統網址new window
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潛在群體模型在分析時,必須嘗試不同群體數來尋找合適群體數,以及嘗試不同起始值來避免不唯一解情況發生,再加上期望最大化(expectation maximization; EM)演算法之運算速度緩慢。所以,本文首先運用最大懲罰概似原則之模糊集群演算法,提出新的潛在群體模型求解策略,來加快潛在群體模型分析速度,並可避免群體數設定過多所造成的分類錯誤。然後,本文透過知識發掘流程,配合中醫疾病分類辨證編碼系統(B-code),對現有系統性紅斑性狼瘡(Systematic Lupus Erythematosus ; SLE)病症患者之辨證進行資料整理。再運用資料探勘中之潛在群體模型,對SLE之辨證資料進行知識發掘,找出經常出現之證型類別。最後,將知識發掘所獲得證型類別之判斷準則,做為建立中醫臨床決策支援系統雛型之依據。
The expectation maximization (EM) algorithms for the latent class model require a number of clusters, and initial values are difficult to determine. Importantly, the EM algorithm has a large number of iterations that require a significant amount of time to solve all possible. This study uses the fuzzy clustering algorithm to present a strategy of algorithm for the latent class model. The latent class models in data mining and disease pattern coding system of traditional Chinese medicine (i.e., B-code) are applied to discover important knowledge of disease patterns. Data obtained from Systematic Lupus Erythematosus (SLE) patients are rearranged with B-code and, then, the latent class model is used to cluster the commonly occurred disease patterns of SLE by B-code. Additionally, the decision rule of disease patterns is adopted as the basis for constructing the clinical decision-support systems of Chinese medicine.
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