Table of Contents

Preface, 1. Clusters: The Human Point of View (HPOV); 2. Uncertainty: Fuzzy sets and models; 3. Clusters: the Computer Point of View (CPOV); 4. The Three Canonical Problems; 5. Feature Analysis; 6. The c-means (aka k-means) Models; 7. Probabilistic Clustering - GMD/EM; 8. Relational Clustering - The SAHN Models; 9. Properties of the Fantastic Four: External Cluster Validity; 10. Alternating Optimization; 11. Clustering in Big Static Data; 12. Structural Assessment in Streaming Data; Lists of Acronyms, Abbreviations, Algorithms, Definitions, Examples, Figures, Tables, Theorems, Video Links; Subject Index.

About the Author

Jim received the BS in Civil Engineering from the University of Nevada (Reno) in 1969; and the PhD in Applied Mathematics from Cornell University in 1973. Jim is past president of NAFIPS (North American Fuzzy Information Processing Society), IFSA (International Fuzzy Systems Association) and the IEEE CIS (Computational Intelligence Society when it was the Neural Networks Council): founding editor the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems: Life fellow of the IEEE and IFSA; recipient of the IEEE 3rd Millennium, IEEE CIS Fuzzy Systems Pioneer, and IEEE technical field award Rosenblatt medals; and the IPMU Kempe de Feret award.