Projection-Based Clustering Through Self-Organization and Swarm Intelligence : Combining Cluster Analysis with the Visualization of High-Dimensional Data. - Thrun, Michael Christoph (Wiesbaden : Springer Fachmedien Wiesbaden GmbH, 2018)
9.2.2.1 Swarm-Organized Projection (SOP) -- 9.2.2.2 Pswarm -- 9.2.3 Common clustering algorithms -- 9.3 Gene Ontology (GO) -- 9.3.1 Overrepresentation Analysis (ORA) -- 9.3.2 Filtering via ABC Analysis -- 10 Results on Pre-classified Data Sets -- 10.1 Comparison with Given Classifications -- 10.1.1 Recognition of the Absence of Clusters -- 10.2 Evaluation of Projections Using the Delaunay Classification Error (DCE) -- 10.3 Topographic Maps with Hypsometric Colors -- 11 DBS on Natural Data Sets -- 11.1 Types of Leukemia -- 11.2 World Gross Domestic Product (World GDP) -- 11.3 Tetragonula Bees -- 12 Knowledge Discovery with DBS -- 12.1 Hydrology -- 12.1.1 Knowledge Acquisition and Prediction in the Hydrology Data Set -- 12.2 Pain Genes -- 12.2.1 Prior Knowledge -- 12.2.2 Knowledge Acquisition in Clusters of Pain Genes -- 13 Discussion -- 14 Conclusion -- References -- Appendices -- Supplement A: Evaluation of Common QMs -- Supplement B: Wine Dataset Distance Distribution -- Supplement C: Generalized Umatrix of Pswarm and SOP -- Supplement D: DBS Visualizations of S-shape and uniform Cuboid -- Supplement E: U-Matrix Visualizations of ESOM Projections -- Supplement F: Statistical Tests in Hydrology -- Supplement G: 3D Prints of Generalized Umatrix Visualizations of DBS -- Supplement H: Contingency Table for Tetragonula Bees Clustering -- Index.
Description based on publisher supplied metadata and other sources.
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Keywords: Electronic books