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Data reduction techniques

data reduction techniques

Errors in the training set propagate in the results, however supervised methods are more accurate.
For example, a hierarchy for branch could allow branches to be grouped into regions, based on their location.
Fortunately, a series of techniques called dimensionality reduction aim to help alleviate these issues.In other words, the lowest level should be used for the analysis.It's most common use is with backups and archives, but it's increasingly gaining acceptance with primary storage.For example, Figure.17 shows the first two principal components, Y1 and Y2, for the given set of data originally mapped to the axes X1 and."A couple sac jump promo years ago, we used to make full-volume copies all the time to make backups more efficient.Principal Components Analysis, principal Components Analysis (PCA) is one of the most common dimensionality reduction methods and is often a starting point for many analyses.Availability of methods widens by having access to a well defined and accurate training set.
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Compression: Data deduplication eliminates redundant data; compression reduces the size of every piece of data based on algorithms that have been around since the 1950s.Dimensionality reduction, encoding mechanisms are used to reduce the dataset size.We don't want to store junk.".For analysts working in fields that deal with extreme number of dimensions, like genomics, reducing dimensions will aid in data compression and storage.PCA is a linear combination of original variables.Feature extraction methods transform the data from high to lower dimensions.This is because LDA is a supervised learning technique, which requires an analyst to provide a properly defined training set for the analysis to learn.

Leading storage vendors generally offer some form of compression.
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The leading vendors in the backup/archive realm all offer data deduplication capabilities.