Exploratory Data Mining and Data Cleaning

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
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