Chapter 19 Unsupervised Methods Clustering Methods

• Main Author: Bacher, Johann (auth)
• Other Authors: Pöge, Andreas (auth), Wenzig, Knut (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: Taylor & Francis 2022
• Subjects: Computer science; Psychology; Psychological methodology; survey data, data analysis, data science, information technology, AI, socio-robotics, quantitative, survey methodology, ethics, ethical standards, privacy, replication, politics, survey design, social media, big data, social, human-robot interaction, machine learning, open data, data archives, data ownership, digital trace, unstructured data
• Online Access: DOAB: download the publication; DOAB: description of the publication

Summary

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

Physical Description

1 electronic resource (19 p.)

ISBN

9781003025245-23
9780367457808
9781032077703

Access

Open Access