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Books > Computing & IT > Applications of computing > Databases > Data mining
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Data Mining in Large Sets of Complex Data (Paperback, 2013 ed.)
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Data Mining in Large Sets of Complex Data (Paperback, 2013 ed.)
Series: SpringerBriefs in Computer Science
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The amount and the complexity of the data gathered by current
enterprises are increasing at an exponential rate. Consequently,
the analysis of Big Data is nowadays a central challenge in
Computer Science, especially for complex data. For example, given a
satellite image database containing tens of Terabytes, how can we
find regions aiming at identifying native rainforests,
deforestation or reforestation? Can it be made automatically? Based
on the work discussed in this book, the answers to both questions
are a sound "yes", and the results can be obtained in just minutes.
In fact, results that used to require days or weeks of hard work
from human specialists can now be obtained in minutes with high
precision. Data Mining in Large Sets of Complex Data discusses new
algorithms that take steps forward from traditional data mining
(especially for clustering) by considering large, complex datasets.
Usually, other works focus in one aspect, either data size or
complexity. This work considers both: it enables mining complex
data from high impact applications, such as breast cancer
diagnosis, region classification in satellite images, assistance to
climate change forecast, recommendation systems for the Web and
social networks; the data are large in the Terabyte-scale, not in
Giga as usual; and very accurate results are found in just minutes.
Thus, it provides a crucial and well timed contribution for
allowing the creation of real time applications that deal with Big
Data of high complexity in which mining on the fly can make an
immeasurable difference, such as supporting cancer diagnosis or
detecting deforestation.
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