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Group Processes : Data-Driven Computational Approaches /

This volume introduces a series of different data-driven computational methods for analyzing group processes through didactic and tutorial-based examples. Group processes are of central importance to many sectors of society, including government, the military, health care, and corporations. Computat...

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Bibliographic Details
Corporate Authors: SpringerLink Online service
Group Author: Pilny, Andrew; Poole, Marshall Scott
Published: Springer International Publishing : Imprint: Springer,
Publisher Address: Cham :
Publication Dates: 2017.
Literature type: eBook
Language: English
Series: Computational Social Sciences,
Subjects:
Computer science
Knowledge management
Big data
Data mining
Computer simulation
Social sciences
Simulation and Modeling
Methodology of the Social Sciences
Big Data/Analytics
Data Mining and Knowledge Discovery
Industrial and Organizational Psychology
Online Access: http://dx.doi.org/10.1007/978-3-319-48941-4
Summary: This volume introduces a series of different data-driven computational methods for analyzing group processes through didactic and tutorial-based examples. Group processes are of central importance to many sectors of society, including government, the military, health care, and corporations. Computational methods are better suited to handle (potentially huge) group process data than traditional methodologies because of their more flexible assumptions and capability to handle real-time trace data. Indeed, the use of methods under the name of computational social science have exploded over the
Carrier Form: 1 online resource (VI, 206 pages) : illustrations.
ISBN: 9783319489414
Index Number: QA76
CLC: TP391.9
Contents: Introduction -- Response Surface Models to Analyze Nonlinear Group Phenomena -- Causal Inference using Bayesian Network -- A Relational Event Approach to Modeling Behavioral Dynamics -- Text Mining Tutorial -- Sequential Synchronization Analysis -- Group Analysis using Machine Learning Techniques -- Simulation and Virtual Experimentation: Grounding with Empirical Data.

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