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OverviewFull Product DetailsAuthor: Luc Anselin (University of Chicago)Publisher: Taylor & Francis Ltd Imprint: Chapman & Hall/CRC Weight: 0.590kg ISBN: 9781032713021ISBN 10: 103271302 Pages: 210 Publication Date: 29 May 2024 Audience: College/higher education , Professional and scholarly , Tertiary & Higher Education , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1. Introduction Part 1: Dimension Reduction 2. Principal Component Analysis (PCA) 3. Multidimensional Scaling (MDS) 4. Stochastic Neighbor Embedding (SNE) Part 2: Classic Clustering 5. Hierarchical Clustering Methods 6. Partioning Clustering Methods 7. Advanced Clustering Methods 8. Spectral Clustering Part 3: Spatial Clustering 9. Spatializing Classic Clustering Methods 10. Spatially Constrained Clustering - Hierarchical Methods 11. Spatially Constrained Clustering - Partitioning Methods Part 4: Assessment 12. Cluster ValidationReviewsAuthor InformationLuc Anselin is the Founding Director of the Center for Spatial Data Science at the University of Chicago, where he is also Stein-Freiler Distinguished Service Professor of Sociology and the College, as well as a member of the Committee on Data Science. He is the creator of the GeoDa software and an active contributor to the PySAL Python open source software library for spatial analysis. He has written widely on topics dealing with the methodology of spatial data analysis, including his classic 1988 text on Spatial Econometrics. His work has been recognized by many awards, such as his election to the U.S. National Academy of Science and the American Academy of Arts and Science. Tab Content 6Author Website:Countries AvailableAll regions |
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