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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.

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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.

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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