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The field of data mining has made significant and far-reaching
advances over the past three decades. Because of its
potential power for solving complex problems, data mining has been
successfully applied to diverse areas such as business,
engineering, social media, and biological science. Many of these
applications search for patterns in complex structural information.
In biomedicine for example, modeling complex biological systems
requires linking knowledge across many levels of science, from
genes to disease. Further, the data characteristics of the problems
have also grown from static to dynamic and spatiotemporal, complete
to incomplete, and centralized to distributed, and grow in their
scope and size (this is known as big data). The effective
integration of big data for decision-making also requires privacy
preservation. The contributions to this monograph summarize the
advances of data mining in the respective fields. This volume
consists of nine chapters that address subjects ranging from mining
data from opinion, spatiotemporal databases, discriminative
subgraph patterns, path knowledge discovery, social media, and
privacy issues to the subject of computation reduction via binary
matrix factorization.
The field of data mining has made significant and far-reaching
advances over the past three decades.Because of its potential power
for solving complex problems, data mining has been successfully
applied to diverse areas such as business, engineering, social
media, and biological science. Many of these applications search
for patterns in complex structural information. In biomedicine for
example, modeling complex biological systems requires linking
knowledge across many levels of science, from genes to disease.
Further, the data characteristics of the problems have also grown
from static to dynamic and spatiotemporal, complete to incomplete,
and centralized to distributed, and grow in their scope and size
(this is known as "big data"). The effective integration of big
data for decision-making also requires privacy preservation.
The contributions to this monograph summarize the advances of
data mining in the respective fields. This volume consists of nine
chapters that address subjects ranging from mining data from
opinion, spatiotemporal databases, discriminative subgraph
patterns, path knowledge discovery, social media, and privacy
issues to the subject of computation reduction via binary matrix
factorization."
The LNCS Journal on Data Semantics is devoted to the
presentation of notable work that addresses research and
development on issues related to data semantics. Based on the
highly visible publication platform Lecture Notes in Computer
Science, this new journal is widely disseminated and available
worldwide. The scope of the journal ranges from theories supporting
the formal definition of semantic content to innovative
domain-specific applications of semantic knowledge.
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