A unique, integrated approach to exploratory data mining and data quality

Data analysts at information-intensive businesses are frequently asked to analyze new data sets that are often dirty–composed of numerous tables possessing unknown properties. Prior to analysis, this data must be cleaned and explored–often a long and arduous task. Ensuring data quality is a notoriously messy problem that can only be addressed by drawing on methods from many disciplines, including statistics, exploratory data mining, database management, and metadata coding.

Where other books on data mining and analysis focus primarily on the last stage of the analysis procedure, Exploratory Data Mining and Data Cleaning uses a uniquely integrated approach to data exploration and data cleaning to develop a suitable modeling strategy that will help analysts to more effectively determine and implement the final technique.

The authors, both seasoned data analysts at a major corporation, draw on their own professional experience to:

• Present a brief overview of the main analytical techniques used in data mining practices, such as univariate and multivariate summaries of attributes and their interactions including Q -Q plots, fractal dimension and histograms, nonparametric approaches incorporating data depth, and more
• Provide numerous references to the related literature on clustering, classification, regression, and more
• Focus on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge
• Address methods of detecting, quantifying (metrics), and correcting data quality issues that significantly impact findings and decisions, using commercially available tools as well as new algorithmic approaches
• Use case studies to illustrate applications in real-life scenarios
• Highlight new approaches and methodologies, such as the DataSphere space partitioning and summary-based analysis techniques

A groundbreaking addition to the existing literature, Exploratory Data Mining and Data Cleaning serves as an important reference for data analysts who need to analyze large amounts of unfamiliar data, operations managers, and students in undergraduate or graduate-level courses dealing with data analysis and data mining.