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研究生:吳姿汶
研究生(外文):Tzu-WenWu
論文名稱:應用分群與離群值偵測技術查核委外工程完成項目之研究
論文名稱(外文):A Study of Applying Clustering and Outlier Detection Methods for Auditing Completed Contract Items
指導教授:翁慈宗翁慈宗引用關係
指導教授(外文):Tzu-Tsung Wong
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工業與資訊管理學系專班
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:44
中文關鍵詞:工程項目查核密度導向分群法距離偵測法離群值分析
外文關鍵詞:Completed contract items auditdensity-based clusteringdistance-based outlier detectionoutlier analysis
相關次數:
  • 被引用被引用:1
  • 點閱點閱:207
  • 評分評分:
  • 下載下載:13
  • 收藏至我的研究室書目清單書目收藏:0
為促進國內經濟發展,改善人民生活品質,帶動民間產業熱絡,大型企業每年編列龐大預算興辦工程,並藉由施工查核機制,來管控品質與成本。於此背景條件下,許多企業發展「工程管理資訊系統」進行管理,但查核仍仰賴人力。本研究將使用資料探勘工具,於大量的施工內容中,偵測出異常資料,進而達到穩定品質、成本控制的目標。首先,我們蒐集工程相關領域的文獻資料,探討現行工程管理及查核制度,針對竣工之工作單內容,進行密度導向分群演算,產出分群結果和離群值,經由側影係數來驗證群集,其係數越大者,則是較理想的分群結果,還需結合專業人士來賦予結果實務意義,才能成為設計人員的有效資訊,而濾出之離群值,則運用距離偵測方法來處理,並設定平均數為門檻值,偵測為異常值的結果與實際修正資料進行比對,工作內容異常偵測準確率達約97%,而誤判率僅約1%,由此證實本研究架構的可行性,確實提出有效的查核方式,不僅能全面性的查核工作單內容,也能有效的控制成本。
In order to promote domestic economic development, improve the quality of people’s life, and create boom in private industry, large enterprises allocate huge constructing budget every year. Audit mechanism is then used to control the quality and cost of construction. Although many enterprises have developed “Engineering Management Information System” to monitor these projects, auditing tasks are generally performed manually. This study attempts to use data mining tools for detecting outliers to assist in auditing construction records such that the quality and the cost of construction can be stabilized. First, we collect the literature in construction-related fields and explore current construction management and auditing mechanisms. Then a density-based clustering algorithm is employed to generate clusters and outliers. The validities of clusters are tested by their silhouette coefficients. The clusters are further grouped by domain experts to reflect their practical meanings. The average distance calculated from the outliers is used as a threshold to divide them into abnormal and rare cases. The experimental results show that the detection rate of abnormal cases can be up to 97% when the false alarm rate is only approximately 1%. These results demonstrate that the framework proposed by this study can assist in auditing construction records to control construction costs effectively.
中文摘要 I
英文摘要 II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究架構 3
1.4 研究範圍 3
第二章 文獻探討 4
2.1 工程品質管理制度 4
2.2 工程品質查核分析 6
2.3 工程資訊系統導入 7
2.4 資料探勘 10
2.4.1 分群處理流程(Clustering procedure) 10
2.4.2 分群方法(Clustering methods) 14
2.4.3 離群值分析(Outlier analysis) 17
第三章 研究方法 19
3.1 資料前置處理 20
3.2 分群演算法及評估測度 23
3.2.1 密度式分群演算 23
3.2.2 群集評估 26
第四章 實證研究 30
4.1 資料前置處理及說明 30
4.2 分群測試及評估 32
4.3 離群值的距離計算及偵測 36
第五章 結論與建議 38
5.1 結論 38
5.2 未來研究方向與建議 40
參考文獻 41
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