Exploratory Data Mining and Data Cleaning
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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.
_ 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.
_ 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.