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 The Census Data System by Philip Rees, "The Census Data System" is a complete resource package for understanding the administration, processing, correction, dissemination and use of Census data. Census data have unique value in demographic and social research, in research into commercial markets and in informing key decisions in public policy formulation. This book gives its readers a head start in understanding and exploiting the uniquely valuable information to be published from the 2001 UK Census. Authored by leading Census researchers, the book is split into six sections that address issues in geography and lookup tables, boundary data and visualization, area statistics, microdata, interaction data and planning for 2001 Census outputs. This book also provides a comprehensive coverage of value-added products of the 1991 Census and a preview of the expanded and improved data products from the 2001 Census. A unique feature of "The Census Data System" is the provision of a CD-ROM that includes data and software to support research, including a full set of 1971-81-91 UK Census data reaggregated to a consistent 1981 ward geography. In addition, the CD-ROM provides a chapter-by-chapter guide to the many official and academic web resources that relate to Census-derived products. The book's dedicated website mirrors the CD-ROM contents and will provide updated links to the many resources identified. "The Census Data System" is an essential resource package for students and researchers who work with population data, and for public and private sector professionals involved with geodemographics in the areas of retailing, transport and property.
 Clustering for Data Mining: A Data Recovery Approach Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical attention that would establish a firm relationship between the two methods and relevant interpretation aids. Rather than the traditional set of ad hoc techniques, Clustering for Data Mining: A Data Recovery Approach presents a theory that not only closes gaps in K-Means and Ward methods, but also extends them into areas of current interest, such as clustering mixed scale data and incomplete clustering. The author suggests original methods for both cluster finding and cluster description, addresses related topics such as principal component analysis, contingency measures, and data visualization.
Data recovery - Data recovery is the process of recovering data from primary storage media when it cannot be accessed normally. This can be due to physical damage to the storage device or logical damage to the file system that prevents it from being mounted by the host operating system. Data Recovery Center - == Definition == Compact flash recovery - Compact flash recovery refers to data recovery from flash memory devices that have had data stored on them corrupted. This can occur from various reasons, though the most common one is due to removing the device while data has been written to it. Backup systems and recovery - Businesses must adopt policies and procedures with regular and effective system backups ensuring that their data is adequately protected. Prevention of lost data should include a multi-tiered approach that considers the risks related to viruses, firewall protection, content filtering, identifying vulnerabilities and management as well as intrusion detection.
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