Behavior Computing

Modeling, Analysis, Mining and Decision
2012
ISBN: 978-1-4471-6206-3
Verlag: Springer

Buch, Englisch, 376 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 593 g

'Behavior' is an increasingly important concept in the scientific, societal, economic, cultural, political, military, living and virtual worlds. Behavior computing, or behavior informatics, consists of methodologies, techniques and practical tools for examining and interpreting behaviours in these various worlds. Behavior computing contributes to the in-depth understanding, discovery, applications and management of behavior intelligence.
With contributions from leading researchers in this emerging field Behavior Computing: Modeling, Analysis, Mining and Decision includes chapters on: representation and modeling behaviors; behavior ontology; behaviour analysis; behaviour pattern mining; clustering complex behaviors; classification of complex behaviors; behaviour impact analysis; social behaviour analysis; organizational behaviour analysis; and behaviour computing applications.
Behavior Computing: Modeling, Analysis, Mining and Decision provides a dedicated source of reference for the theory and applications of behavior informatics and behavior computing. Researchers, research students and practitioners in behavior studies, including computer science, behavioral science, and social science communities will find this state of the art volume invaluable.

Zielgruppe

Research

Autoren/Hrsg.

Yu, Philip S.

Cao, Longbing

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme

Inhaltsverzeichnis

Preface.- Part I: Behavior Modeling.- Analyzing Behavior of the Influentials across Social Media.- Modeling and Analysis of Social Activity Process.- Behavior Representation and Management Making Use of the Narrative Knowledge Representation Language.- Semi-Markovian representation of User Behavior in Software Packages.- Part II: Behavior Analysis.- P-SERS: Personalized Social Event Recommender System.- Simultaneously Modeling Reply Networks and Contents to Generate User’s Profiles on Web Forum.- Information Searching Behavior Mining Based on Reinforcement Learning Models.- Estimating Conceptual Similarities using Distributed Representations and Extended Backpropagation.- Scoring and Predicting Risk Preferences.- An Introduction to Prognostic Search.- Part III: Behavior Mining.- Clustering Clues of Trajectories for Discovering Frequent Movement Behaviors.- Linking Behavioral Patterns to Personal Attributes through Data Re-Mining.- Mining Causality from Non-categorical Numerical Data.- A Fast Algorithm for Mining High Utility Itemsets.- Individual Movement Behavior in Secure Physical Environments: Modeling and Detection of Suspicious Activity.- A Behavioral Modeling Approach to Prevent Unauthorized Large-Scale Documents Copying from Digital Libraries.- Analyzing Twitter User Behaviors and Topic Trends by Exploiting Dynamic Rules.- Part IV: Behavior Applications.- Behavior Analysis of Telecom Data using Social Network Analysis.- Event Detection based on Call Detail Records.- Smart Phone: Predicting the Next Call.- A System with Hidden Markov Models and Guassian Mixture Models for 3D Handwriting Recognition on Handheld Devices using Accelerometers.- Medical Student’s Search Behavior: An Exploratory Survey.- An Evaluation Scheme of Software Testing Strategy.