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."
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