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詳目顯示 ; Chi-Ming Tsou · 智慧型商業資料分析之研究 · On Some Aspecsts of Intelligent Business Data Analysis · 黃登源;黃榮華 · Teng-Yuang Huang;Rong-Hwa Huang. 資料載入處理中... 跳到主要內容 臺灣博碩士論文加值系統 ::: 網站導覽| 首頁| 關於本站| 聯絡我們| 國圖首頁| 常見問題| 操作說明 English |FB專頁 |Mobile 免費會員 登入| 註冊 功能切換導覽列 (178.128.221.219)您好！臺灣時間：2021/12/2813:24 字體大小：       ::: 詳目顯示 recordfocus 第1筆/ 共1筆  /1頁 論文基本資料 摘要 外文摘要 目次 參考文獻 電子全文 紙本論文 QRCode 本論文永久網址: 複製永久網址Twitter研究生:鄒濟民研究生(外文):Chi-MingTsou論文名稱:智慧型商業資料分析之研究論文名稱(外文):OnSomeAspecstsofIntelligentBusinessDataAnalysis指導教授:黃登源、黃榮華指導教授(外文):Teng-YuangHuang、Rong-HwaHuang學位類別:博士校院名稱:輔仁大學系所名稱:商學研究所學門:商業及管理學門學類:一般商業學類論文種類:學術論文論文出版年:2006畢業學年度:94語文別:中文論文頁數:103中文關鍵詞:資料分析、知識發現、資料探勘外文關鍵詞:DataAnalysis、KnowledgeDiscovery、DataMining相關次數: 被引用:2點閱:1319評分:下載:358書目收藏:3 資料分析是由資料探測智慧的必經途徑，然而在商業的應用上卻充滿著各種挑戰，因而智慧型商業資料分析，就是針對一多變量的資料集，進行高層次概念分析模型的建立。

此一結合統計與資料探勘運算技術的工具，克服了商業應用上常面臨的資料集變動頻繁、資料衡量尺度種類很多、資料欄位過多、資料欄位間關係複雜及模型配適不易等諸多問題，且所得出的模型具有統計上的可靠性，並能供領域專家很容易的進行解釋與評估。

換言之，智慧型商業資料分析就是針對商業上的應用，結合了統計與資料探勘運算技術，且能滿足知識周全性原則的資料分析方法。

本研究以三種基本知識概念，包括關聯規則、結構方程式及列聯表等在商業上的應用，包括市場購物籃分析、知識的發現與創新及企業經營績效評量等問題，來進行智慧型商業資料分析方法與模型建構的探討。

本研究的主要目的即在發展智慧型商業資料分析方法及建構分析模型，這些模型克服了傳統統計與資料探勘方法在本質上的限制，滿足了知識周全性的原則，而將其整合在一個工具中，這些工具提供了使用者，可依問題的特性由資料庫中進行各種不同方式的知識發現工作，來建構可供解釋與決策所需的模型，以解決實務上所面臨的諸多問題，此即為本研究的主要貢獻。

Dataanalysisisontheuniqueroutefromdatatowardswisdom,whilethereexistsvariouschallengesforbusinessapplications,hence;intelligentbusinessdataanalysisisjustlikeakeytoopenadoorwhichcangetoverthosechallengesduringproceedinghighlevelconceptualandanalyticalmodelconstructionagainstamulti-dimensionaldataset.Thosedataminingtoolsincorporatestatisticandcomputationaltechnologiescansolvetheproblemsthatencounteredinbusinessapplicationssuchaslargedatavolume,incompletedata,inconsistentdatascale,hugenumberofdatafields,complexrelationshipsbetweendatafieldsandmodeloverfitting;andtheresultingmodelsthatweobtainedwillbebuiltwithstatisticalreliability,andcanbeinterpretedandevaluatedeasilybydomainexperts.Inotherwords,intelligentbusinessdataanalysisistheadvancedknowledgediscoverytoolswhichincorporatestatisticandcomputation,andfulfillsthecomprehensivenessruleofknowledge.Theresearchadopts3basicknowledgeconceptsincludingassociationrule,structuralequationandcontingencytable,withtheircorrespondingapplicationsonbusinessasmarketbasketanalysis,knowledgediscoveringandinnovationandperformanceevaluation,toproceedthecontextexplorationonintelligentbusinessdataanalysis.Themainpurposeofthisstudyistodeveloptheanalyticalmethodsandmodelbuildingforintelligentbusinessdataanalysis.Thoseanalyticalmodelsnotonlyovercomethelimitationsoftraditionalstatisticanddataminingmethods,butalsofulfillthecomprehensivenessruleofknowledge;andincorporatethemintoonetoolsset,thosetoolsfurnishthecapabilityforusertoproceedvariousknowledgediscoverytasksbyusingdatabasebasedonthedistinctionofproblems.Modelsestablishedbythosetoolscanbeinterpretedandareusefulfordecisionmakingtosolvetheproblemsthatweencounteredintherealworld,anditisourcontributiontothefieldofintelligentbusinessdataanalysis. 第一章緒論11.1研究背景11.2研究動機與目的21.3研究架構與流程4第二章文獻探討72.1由資料探測智慧之過程72.1.1知識的特徵82.1.2知識的表現格式92.2商業資料探勘112.2.1知識發現的程序與目的132.2.2資料探勘在商業應用上的挑戰142.3多變量商業資料分析方法162.3.1關聯規則相關研究162.3.2創新知識探測182.3.3績效評量考核21第三章關聯規則研究263.1局部關聯規則之建構263.1.1關聯資訊量計算273.1.2建立項目關聯聚類323.2屬性局部關聯分類法363.3關聯規則案例探討423.3.1市場購物籃案例分析423.3.2多屬性資料集局部關聯443.3.3多屬性資料集局部關聯規則分類46第四章潛在結構模型探測研究504.1潛在結構模型之探測過程504.1.1尺度不變性(ScaleInvariant)524.1.2潛在結構矩陣(LatentStructureMatrix，LSM)524.1.3結構殘差矩陣(StructureResidualMatrix，SRM)544.2潛在結構模型建構程序544.3潛在結構模型探測案例探討564.3.1完整變數集潛在結構矩陣574.3.2部份變數集潛在結構矩陣60第五章績效評量研究625.1績效衡量與評估方法625.1.1尺度主成份分析635.1.2效率評估矩陣655.1.3效率評估排序法675.1.4效率對應分析675.1.5效率評估與排序程序685.2績效評量案例探討705.2.1效率對應與排序715.2.2效率評估矩陣線性規劃模型755.3績效排序探討765.3.1供應商評選765.3.2製造設備評選825.3.3藥局績效評估84第六章結論與建議906.1結論906.1.1局部關聯規則916.1.2潛在結構模型探測916.1.3績效衡量評估926.2建議946.2.1局部關聯規則946.2.2潛在結構模型探測956.2.3績效衡量評估95參考文獻97 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