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Data Mining: Concepts and Techniques, Fourth Edition introduces
concepts, principles, and methods for mining patterns, knowledge,
and models from various kinds of data for diverse applications.
Specifically, it delves into the processes for uncovering patterns
and knowledge from massive collections of data, known as knowledge
discovery from data, or KDD. It focuses on the feasibility,
usefulness, effectiveness, and scalability of data mining
techniques for large data sets. After an introduction to the
concept of data mining, the authors explain the methods for
preprocessing, characterizing, and warehousing data. They then
partition the data mining methods into several major tasks,
introducing concepts and methods for mining frequent patterns,
associations, and correlations for large data sets; data
classificcation and model construction; cluster analysis; and
outlier detection. Concepts and methods for deep learning are
systematically introduced as one chapter. Finally, the book covers
the trends, applications, and research frontiers in data mining.
Networks naturally appear in many high-impact domains, ranging from
social network analysis to disease dissemination studies to
infrastructure system design. Within network studies, network
connectivity plays an important role in a myriad of applications.
The diversity of application areas has spurred numerous
connectivity measures, each designed for some specific tasks.
Depending on the complexity of connectivity measures, the
computational cost of calculating the connectivity score can vary
significantly. Moreover, the complexity of the connectivity would
predominantly affect the hardness of connectivity optimization,
which is a fundamental problem for network connectivity studies.
This book presents a thorough study in network connectivity,
including its concepts, computation, and optimization.
Specifically, a unified connectivity measure model will be
introduced to unveil the commonality among existing connectivity
measures. For the connectivity computation aspect, the authors
introduce the connectivity tracking problems and present several
effective connectivity inference frameworks under different network
settings. Taking the connectivity optimization perspective, the
book analyzes the problem theoretically and introduces an
approximation framework to effectively optimize the network
connectivity. Lastly, the book discusses the new research frontiers
and directions to explore for network connectivity studies. This
book is an accessible introduction to the study of connectivity in
complex networks. It is essential reading for advanced
undergraduates, Ph.D. students, as well as researchers and
practitioners who are interested in graph mining, data mining, and
machine learning.
This book constitutes the refereed proceedings of the 8th
International Conference on Computational Data and Social Networks,
CSoNet 2019, held in Ho Chi Minh City, Vietnam, in November 2019.
The 22 full and 8 short papers presented in this book were
carefully reviewed and selected from 120 submissions. The papers
appear under the following topical headings: Combinatorial
Optimization and Learning; Influence Modeling, Propagation, and
Maximization; NLP and Affective Computing; Computational Methods
for Social Good; and User Profiling and Behavior Modeling.
Business operations in large organizations today involve massive,
interactive, and layered networks of teams and personnel
collaborating across hierarchies and countries on complex tasks. To
optimize productivity, businesses need to know: what communication
patterns do high-performing teams have in common? Is it possible to
predict a team's performance before it starts work on a project?
How can productive team behavior be fostered? This comprehensive
review for researchers and practitioners in data mining and social
networks surveys recent progress in the emerging field of network
science of teams. Focusing on the underlying social network
structure, the authors present models and algorithms
characterizing, predicting, optimizing, and explaining team
performance, along with key applications, open challenges, and
future trends.
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