[求助]Tamraparni Dasu,Theodore Johnson.Exploratory Data Mining and Data Cleaning

相似文件 换一批

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

Dasu, Tamraparni / Johnson, Theodore Exploratory Data Mining and Data Cleaning Wiley Series in Probability and Statistics 1. Edition - June 2003 67.90 Euro / 109.- SFR 2003. 224 Pages, Hardcover ISBN 0-471-26851-8 - John Wiley & Sons Short description Many current books on data mining and analysis focus on the last stage of the analysis process (getting the results) and spend little time on the data exploration and data cleaning processes. The true challenge in data mining is creating a set that contains relevant and accurate information and determining the appropriate analysis techniques. This timely reference develops a systematic process of data exploration, data cleaning, and evolving a suitable modeling strategy to help analysts determine and implement a "final" technique. From the contents 0.1 Preface. 1 Exploratory Data Mining and Data Cleaning: An Overview. 1.1 Introduction. 1.2 Cautionary Tales. 1.3 Taming the Data. 1.4 Challenges. 1.5 Methods. 1.6 EDM. 1.6.1 EDM Summaries - Parametric. 1.6.2 EDM Summaries - Nonparametric. 1.7 End­to­End Data Quality (DQ). 1.7.1 DQ in Data Preparation. 1.7.2 EDM and Data Glitches. 1.7.3 Tools for DQ. 1.7.4 End­to­End DQ: The Data Quality Continuum. 1.7.5 Measuring Data Quality. 1.8 Conclusion. 2 Exploratory Data Mining. 2.1 Introduction. 2.2 Uncertainty. 2.2.1 Annotated Bibliography. 2.3 EDM: Exploratory Data Mining. 2.4 EDM Summaries. 2.4.1 Typical Values. 2.4.2 Attribute Variation. 2.4.3 Example. 2.4.4 Attribute Relationships. 2.4.5 Annotated Bibliography. 2.5 What Makes a Summary Useful? 2.5.1 Statistical Properties. 2.5.2 Computational Criteria. 2.5.3 Annotated Bibliography. 2.6 Data­Driven Approach - Nonparametric Analysis. 2.6.1 The Joy of Counting. 2.6.2 Empirical Cumulative Distribution Function (ECDF). 2.6.3 Univariate Histograms. 2.6.4 Annotated Bibliography. 2.7 EDM in Higher Dimensions. 2.8 Rectilinear Histograms. 2.9 Depth and Multivariate Binning. 2.9.1 Data Depth. 2.9.2 Aside: Depth­Related Topics. 2.9.3 Annotated Bibliography. 2.10 Conclusion. 3 Partitions and Piecewise Models. 3.1 Divide and Conquer. 3.1.1 Why Do We Need Partitions? 3.1.2 Dividing Data. 3.1.3 Applications of Partition­based EDM Summaries. 3.2 Axis­Aligned Partitions and Data Cubes. 3.3 Nonlinear Partitions. 3.3.1 Annotated Bibliography. 3.4 DataSpheres (DS). 3.4.1 Layers. 3.4.2 Data Pyramids. 3.4.3 EDM Summaries. 3.4.4 Annotated Bibliography. 3.5 Set Comparison Using EDM Summaries. 3.5.1 Motivation. 3.5.2 Comparison Strategy. 3.5.3 Statistical Tests for Change. 3.5.4 Application - Two Case Studies. 3.5.5 Annotated Bibliography. 3.6 Discovering Complex Structure in Data with EDM Summaries. 3.6.1 Exploratory Model Fitting in Interactive Response Time. 3.6.2 Annotated Bibliography. 3.7 Piecewise Linear Regression. 3.7.1 An Application. 3.7.2 Regression Coefficients. 3.7.3 Improvement in Fit. 3.7.4 Annotated Bibliography. 3.8 One­Pass Classification. 3.8.1 Quantile­Based Prediction with Piecewise Models. 3.8.2 Simulation Study. 3.8.3 Annotated Bibliography. 3.9 Conclusion. 4 Data Quality. 4.1 Introduction. 4.2 The Meaning of Data Quality. 4.2.1 An Example. 4.2.2 Data Glitches. 4.2.3 Gaps in Time Series Records. 4.2.4 Conventional Definition. 4.2.5 Times Have Changed. 4.2.6 Annotated Bibliography. 4.3 Updating DQ Metrics: Data Quality Continuum. 4.3.1 Data Gathering. 4.3.2 Data Delivery. 4.3.3 Data Monitoring. 4.3.4 Data Storage. 4.3.5 Data Integration. 4.3.6 Data Retrieval. 4.3.7 Data Mining/Analysis. 4.3.8 Annotated Bibliography. 4.4 The Meaning of Data Quality Revisited. 4.4.1 Data Interpretation. 4.4.2 Data Suitability. 4.4.3 Dataset Type. 4.4.4 Attribute Type. 4.4.5 Application Type. 4.4.6 Data Quality - A Many Splendored Thing. 4.4.7 Annotated Bibliography. 4.5 Measuring Data Quality. 4.5.1 DQ Components and Their Measurement. 4.5.2 Combining DQ Metrics. 4.6 The DQ Process. 4.7 Conclusion. 4.7.1 Four Complementary Approaches. 4.7.2 Annotated Bibliography. 5 Data Quality: Techniques and Algorithms. 5.1 Introduction. 5.2 DQ Tools Based on Statistical Techniques. 5.2.1 Missing Values. 5.2.2 Incomplete Data. 5.2.3 Outliers. 5.2.4 Time Series Outliers: A Case Study. 5.2.5 Goodness­of­Fit. 5.2.6 Annotated Bibliography. 5.3 Database Techniques for DQ. 5.3.1 What is a Relational Database? 5.3.2 Why Are Data Dirty? 5.3.3 Extraction, Transformation, and Loading (ETL). 5.3.4 Approximate Matching. 5.3.5 Database Profiling. 5.3.6 Annotated Bibliography. 5.4 Metadata and Domain Expertise. 5.4.1 Lineage Tracing. 5.4.2 Annotated Bibliography. 5.5 Measuring Data Quality? 5.5.1 Inventory Building - A Case Study. 5.5.2 Learning and Recommendations. 5.6 Data Quality and Its Challenges.

[此贴子已经被作者于2005-8-1 11:02:44编辑过]

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：Exploratory Data Mining Cleaning Theodore Johnson Mining Data Johnson Cleaning Theodore

Customers who bought this book also bought

Enterprise Knowledge Management: The Data Quality Approach by David Loshin

Data Quality: The Accuracy Dimension by Jack E. Olson

Data Preparation for Data Mining by Dorian Pyle

The Elements of Statistical Learning by T. Hastie Agile

[此贴子已经被作者于2005-7-16 7:56:38编辑过]