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FCA is an important formalism that is associated with a variety of
research areas such as lattice theory, knowledge representation,
data mining, machine learning, and semantic Web. It is
successfully exploited in an increasing number of application
domains such as software engineering, information retrieval, social
network analysis, and bioinformatics. Its mathematical power comes
from its concept lattice formalization in which each element in the
lattice captures a formal concept while the whole structure
represents a conceptual hierarchy that offers browsing, clustering
and association rule mining. Complex data analytics refers to
advanced methods and tools for mining and analyzing data with
complex structures such as XML/Json data, text and image data,
multidimensional data, graphs, sequences and streaming data. It
also covers visualization mechanisms used to highlight the
discovered knowledge. This edited book examines a set of important
and relevant research directions in complex data management, and
updates the contribution of the FCA community in analyzing
complex and large data such as knowledge graphs and
interlinked contexts. For example, Formal Concept Analysis
and some of its extensions are exploited, revisited and coupled
with recent processing parallel and distributed paradigms to
maximize the benefits in analyzing large data.
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