《文獻(xiàn)查閱》檢索實(shí)習(xí)報(bào)告樣例

1、文獻(xiàn)查閱檢索實(shí)習(xí)報(bào)告班級(jí)： 姓名： 學(xué)號(hào)： 成績(jī)：畢業(yè)設(shè)計(jì)課題名稱：粗糙集理論在數(shù)據(jù)挖掘中的應(yīng)用一、期刊文章的網(wǎng)絡(luò)檢索要求：分別利用中國(guó)知網(wǎng)、萬(wàn)方數(shù)據(jù)庫(kù)、重慶維普數(shù)據(jù)庫(kù)、Elsevier數(shù)據(jù)庫(kù)各檢索1篇文獻(xiàn)， 記錄其外表特征和關(guān)鍵詞，并分別用篇名途徑、關(guān)鍵詞途徑和全文途徑檢索所得文獻(xiàn)的篇數(shù)。1.中國(guó)知網(wǎng)同時(shí)利用“粗糙集”和“數(shù)據(jù)挖掘”作為關(guān)鍵詞進(jìn)行檢索，檢索結(jié)果如下：外部特征篇名基于粗糙集理論的數(shù)據(jù)挖掘模型作者李永敏.朱善君.陳湘暉.張岱崎.韓曾晉.機(jī)構(gòu)清華大學(xué)自動(dòng)化系.刊名清華大學(xué)學(xué)報(bào)（自然科學(xué)版）年卷期1999年01期內(nèi)容特征關(guān)鍵詞粗糙集；知識(shí)發(fā)現(xiàn)；數(shù)據(jù)挖掘；決策系統(tǒng).文獻(xiàn)篇數(shù)關(guān)鍵詞途徑1

2、36 （個(gè)）篇名途徑28（個(gè)）全文途徑808 （個(gè)）2.萬(wàn)方數(shù)據(jù)庫(kù)檢索結(jié)果如下:外部特征篇名基于粗糙集理論的數(shù)據(jù)挖掘模型作者韓曾晉；張岱崎；陳湘暉；朱善君；李永敏機(jī)構(gòu)清華大學(xué)自動(dòng)化系刊名清華大學(xué)學(xué)報(bào)（自然科學(xué)版）年卷期1999 Vol.39 No.1內(nèi)容特征關(guān)鍵詞粗糙集；知識(shí)發(fā)現(xiàn)；數(shù)據(jù)挖掘；決策系統(tǒng)文獻(xiàn)篇數(shù)關(guān)鍵詞途徑85（個(gè)）篇名途徑24（個(gè)）全文途徑123 （個(gè)）3.重慶維普數(shù)據(jù)庫(kù)檢索結(jié)果為：外部特征篇名由規(guī)則歸納系統(tǒng)中發(fā)掘感興趣模式作者馬昕孫優(yōu)賢機(jī)構(gòu)浙江大學(xué)工業(yè)控制技術(shù)國(guó)家重點(diǎn)實(shí)驗(yàn)室刊名計(jì)算機(jī)應(yīng)用.年卷期2003.023（004）內(nèi)容特征關(guān)鍵詞規(guī)則歸納系統(tǒng)感興趣模式數(shù)據(jù)挖掘數(shù)據(jù)庫(kù)知識(shí)發(fā) 現(xiàn)

3、興趣度興趣模板粗糙集文獻(xiàn)篇數(shù)關(guān)鍵詞途徑73（個(gè)）篇名途徑5（個(gè)）4.Elsevier數(shù)據(jù)庫(kù)同時(shí)利用Rough Set和Data Mining作為關(guān)鍵詞進(jìn)行檢索，檢索結(jié)果如下:外部特征篇名Tree structure for efficient data mining using rough sets作者Ananthanarayana,V.S.a;Narasimha Murty,M.a;Subramanian, D.K.a機(jī)構(gòu)a. Department of Computer Science and Automation, Indian Institute of Science, Bangalo

4、re 560 012, India刊名Pattern Recognition Letters年卷期Vol: 24, Issue: 6, March, 2003內(nèi)容特征關(guān)鍵詞PC-tree; Single database scan; Dynamic mining; Segment PC-tree; Rough PC-tree; Classification; Rough set文獻(xiàn)篇數(shù)關(guān)鍵詞途徑16（個(gè)）篇名途徑3（個(gè)）二、論文的網(wǎng)絡(luò)檢索要求：利用ProQuest全文數(shù)據(jù)庫(kù)檢索1篇論文，記錄其外表特征和內(nèi)容特征。同時(shí)利用 Rough Set和 Data Mining 作為關(guān)鍵詞進(jìn)行檢索，檢索結(jié)

5、果如下：外部特征篇名An architecture for a diagnostic/prognostic system with rough set feature selection and diagnostic decision fusion capabilities.作者Lee, Seungkoo.機(jī)構(gòu)Georgia Institute of Technology.年2002內(nèi)容特征摘要This research aims, by applying data mining techniques, at providing a systematic framework to identi

6、fy the most relevant input features from a set of predefined features that correspond to a specific abnormality. An innovative and generalized combination method taking only the advantages of each technique is introduced.; An innovative and generalized combination method taking only the advantages o

7、f each technique is introduced.; An innovative and generalized combination method taking only the advantages of each technique is introduced.; An innovative and generalized combination method taking only the advantages of each technique is introduced.; An innovative and generalized combination metho

8、d taking only the advantages of each technique is introduced.; An innovative and generalized combination method taking only the advantages of each technique is introduced.; An innovative and generalized combination method taking only the advantages of each technique is introduced.; Specifically, the

9、 major contributions of this research includes the followings: feature preparation methods to obtain potential features from raw data, rough set based feature selection methods for FDI and FP, diagnostic rule generation using rough set methods to provide the structure of a classifier, a classificati

10、on tool named Arrangement Fuzzy Neural Network Classifier (AFNNC) to increase the flexibility of the diagnostic module design, and an innovative diagnostic decision method based on Dempster-Shafer evidential theory and weighting fusion technique to increase diagnostic accuracy.; To demonstrate the f

11、easibility of the methodology in practical use, the proposed methods are applied to three different applications: a Navy chiller system, Process Demonstrator, and an automotive backlight inspection system.三、參考文獻(xiàn)著錄要求：按照畢業(yè)論文所要求的參考文獻(xiàn)格式著錄10篇中文參考文獻(xiàn)，5篇英文參考文獻(xiàn)。具體文獻(xiàn)類型 不限。侯榮濤，聞邦椿，周飆.基于現(xiàn)代非線性理論的汽輪發(fā)電機(jī)組故障診斷技術(shù)研究J.

12、機(jī)械工程學(xué)報(bào)，2005, 41(2)： 142-147.張彼德，鄭高.概率因果聯(lián)接模型在汽輪發(fā)電機(jī)組振動(dòng)故障診斷應(yīng)用J.汽輪機(jī)技術(shù)，2004, 46(3)：207-209.黃:撰發(fā)現(xiàn)：輪振動(dòng)M的粗糙集模型J.電力系統(tǒng)自動(dòng)化，2004, 28(15)： 80-84.王國(guó)胤.Rough集理論與知識(shí)獲取M.西安：西安交通大學(xué)出版社，2001.梁吉業(yè)，曲開社.信息系統(tǒng)的屬性約簡(jiǎn)J.系統(tǒng)工程理論與實(shí)踐，2001，21(12)： 76-80.張文修.粗糙集理論與方法M.北京：科學(xué)出版社，2001.閻平凡.人工神經(jīng)網(wǎng)絡(luò)與模擬進(jìn)化計(jì)算M.北京：清華大學(xué)出版社，2000.李化，岳剛.汽輪發(fā)電機(jī)組振動(dòng)故障診斷的模

13、糊輸入方法J.重慶大學(xué)學(xué)報(bào)(自然科學(xué)版)，1999,22(6)：36-40.劉清.Rough集理論及Rough推理M .北京：機(jī)械工業(yè)出版社，2002.TSUMOTO S ET AL. Extraction of domain knowledge from databases based on rough set theoryJ. Fuzzy Set，1996，34(6)： 67-76.Z.PAWLAK. Rough sets J. International Journal of Computer and Information Science， 1982， 11(5)：341-356.乙 PAWLAK. Rough sets: Theoretical Aspects of Reasoning about Data M. Dordrecht： KluwerAcademic Pu