Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.JURECEKOVA and SEN Am Robust Statistical Procedures: Aymptotics and Interrelations JUREK and MASON Am Operator-Limit ... SCHUM Am A Probabilistic Analysis of the Sacco and Vanzetti Evidence KALBFLEISCH and PRENTICE Am The Statistical Analysis of Failure ... Loss Models: From Data to Decisions KLUGMAN, PANJER, and WILLMOT Am Solutions Manual to Accompany Loss Models: From Data toanbsp;...
|Title||:||Exploratory Data Mining and Data Cleaning|
|Author||:||Tamraparni Dasu, Theodore Johnson|
|Publisher||:||John Wiley & Sons - 2003-08-15|