Exploratory Data Mining and Data Cleaning

Exploratory Data Mining and Data Cleaning is written for practitioners of data mining, data cleaning, and database management. The book presents a technical treatment of data quality, including process, metrics, tools, and algorithms.

Focusing on developing an evolving modelling strategy through an iterative data exploration loop and incorporation of domain knowledge, the book addresses methods of detecting, quantifying, and correcting data quality issues that can significantly impact findings and decisions. It explores the use of commercially available tools alongside new algorithmic approaches.

Case studies are utilised to illustrate applications in real-life scenarios. The book also 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 analyse large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large-scale data analysis and data mining.