Information-Statistical Data Mining: Warehouse Integration With Examples of Oracle Basics (The Kluwer International Series in Engineering and Computer Science, 757)
Information-Statistical Data Mining: Warehouse Integration With Examples of Oracle Basics (The Kluwer International Series in Engineering and Computer Science, 757)
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信息统计数据挖掘：仓库集成与 Oracle 基础知识示例（Kluwer 国际工程与计算机科学系列，757）
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发表时间：
2003
期刊：
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通讯作者：
Arjun K. Gupta
中科院分区：
文献类型：
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作者：
B. Sy;Arjun K. Gupta
灵感。奉献精神。贡献作者和联系信息。前言。1：预览：数据仓库/挖掘。1. 什么是摘要信息？2. 数据、信息论、统计学。数据仓库/挖掘管理。架构、工具和应用。概念/实用的挖掘工具。结论2：数据仓库基础。方法。2。结论3：模式的概念与可视化。介绍。附录：文字问题解决方案。4 .信息论与统计学。介绍。2。信息理论。3. 变量相互依赖度量。4. 概率模型比较。5. 皮尔逊卡方统计量。5：信息统计联动。统计。2。信息的概念。3. 信息论与统计学。6：时空数据。介绍。2。时空特征。3. 时空数据分析。4. 问题公式化。5. 温度分析应用。6. 讨论。7。结论7：变化点检测技术。变更点问题。2. 信息标准方法。3. 二值分割技术。4. 例8：统计关联模式。信息统计协会；结论9：模式推断与模型发现。介绍。2。基于模式的推理概念。3. 结论。附录：模式实用程序说明。10：贝叶斯网络与模型生成。贝叶斯网络的初步研究。模型学习的模式综合。结论。11：模式排序推理：第一部分。12：模式排序推理：第二部分。1. 一般事件模式排序。结论。附录一：51个最大的PR（ADHJ BCE | F G）。附录二：订购PR（LGBPY/YGBP | SE）。SE=F G I.附录III。A：方法A的评价。B：方法B的评价。附录III.C：方法c的评价。13：案例研究1:Oracle数据仓库介绍。2。背景。3。挑战。4。插图。5。结论。附录1：仓库数据词典。14：案例研究2：财务数据分析。数据。2. 信息理论方法。3. 数据分析。15：案例研究3：森林分类。介绍。2。分类器模型派生。3. 测试数据特性。4. 实验平台。5. 分类结果。6. 验证阶段。7. 混合数据对性能的影响。8. 用于评价的优度度量。9. 结论。参考文献。索引。Web资源：http://www.techsuite.net/kluwer/Web访问的科学数据仓库示例。变化点检测的MathCAD实现。S-PLUS统计协会开源代码。互联网可下载的模型发现工具。
Inspiration. Dedication. Contributing Authors and Contact Information. Preface. Acknowledgments. 1: Preview: Data Warehousing/Mining. 1. What Is Summary Information? 2. Data, Information Theory, Statistics. 3. Data Warehousing/Mining Management.4. Architecture, Tools And Applications. 5. Conceptual/Practical Mining Tools. 6. Conclusion. 2: Data Warehouse Basics. 1. Methodology. 2. Conclusion. 3: Concept of Patterns & Visualization. 1. Introduction. Appendix: Word problem solution. 4: Information Theory & Statistics. 1. Introduction. 2. Information theory. 3. Variable interdependence measure. 4. Probability model comparison. 5. Pearson's Chi-Square statistic. 5: Information and Statistics Linkage. 1. Statistics. 2. Concept of information. 3. Information theory and statistics. 6: Temporal-Spatial Data. 1. Introduction. 2. Temporal-spatial characteristics. 3. Temporal-spatial data analysis. 4. Problem formulation. 5. Temperature analysis application. 6. Discussion. 7. Conclusion. 7: Change Point Detection Techniques. 1. Change point problem. 2. Information criterion approach. 3. Binary segmentation technique. 4. Example. 8: Statistical Association Patterns. 1. Information-Statistical Association. 2. Conclusion. 9: Pattern Inference & Model Discovery. 1. Introduction. 2. Concept of pattern-based inference. 3. Conclusion. Appendix: Pattern utility illustration. 10: Bayesian Nets & Model Generation. 1. Preliminary of Bayesian Networks. 2. Pattern Synthesis for MODEL Learning. 3. Conclusion. 11: Pattern Ordering Inference: Part I. 12: Pattern Ordering Inference: Part II. 1. Ordering General Event Patterns. 2. Conclusion. Appendix I: 51 largest PR(ADHJ BCE | F G ). Appendix II: ordering Of PR(LGBPY/YGBP | SE). SE=F G I. Appendix III.A: Evaluation of Method A. Appendix III.B: Evaluation of Method B. Appendix III.C: Evaluation of Method C. 13: Case Study 1: Oracle Data Warehouse. 1. Introduction. 2. Background. 3. Challenge. 4. Illustrations. 5. Conclusion. Appendix I: Warehouse Data Dictionary. 14: Case Study 2: Financial Data Analysis. 1. The data. 2. Information theoretic approach. 3. data analysis. 15: Case Study 3: Forest Classification. 1. Introduction. 2. Classifier model derivation. 3. Test data characteristics. 4. Experimental platform. 5. Classification results. 6. Validation stage. 7. Effect of mixed data on performance. 8. Goodness measure for evaluation. 9. Conclusion. References. Index. Web resource: http://www.techsuite.net/kluwer/ 1. Web Accessible Scientific Data Warehouse Example. 2. MathCAD Implementation of Change Point Detection. 3. S-PLUS open source code for Statistical Association. 4. Internet Downloadable Model Discovery Tool. 5.