This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences.
This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences.
Jacob Kogan is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County. Dr. Kogan received his PhD in Mathematics from Weizmann Institute of Science, has held teaching and research positions at the University of Toronto and Purdue University. His research interests include Text and Data Mining, Optimization, Calculus of Variations, Optimal Control Theory, and Robust Stability of Control Systems. Dr. Kogan is the author of Bifurcations of Extremals in Optimal Control and Robust Stability and Convexity: An Introduction. Since 2001, he has also been affiliated with the Department of Computer Science and Electrical Engineering at UMBC. Dr. Kogan is a recipient of 2004-2005 Fulbright Fellowship to Israel. Together with Charles Nicholas of UMBC and Marc Teboulle of Tel-Aviv University he is co-editor of the volume Grouping Multidimensional Data: Recent Advances in Clustering.
Inhaltsangabe
1. Introduction and motivation 2. Quadratic k-means algorithm 3. BIRCH 4. Spherical k-means algorithm 5. Linear algebra techniques 6. Information-theoretic clustering 7. Clustering with optimization techniques 8. k-means clustering with divergence 9. Assessment of clustering results 10. Appendix: Optimization and Linear Algebra Background 11. Solutions to selected problems.
