Algorithms for Clustering Data
by Anil K. Jain, Richard C. Dubes
Publisher: Prentice Hall 1988
Number of pages: 334
This book will be useful for those in the scientific community who gather data and seek tools for analyzing and interpreting data. It will be a valuable reference for scientists in a variety of disciplines and can serve as a textbook for a graduate course in exploratory data analysis.
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by Marko Petkovsek, Herbert S. Wilf, Doron Zeilberger - AK Peters, Ltd.
The book shows how some computer algorithms can simplify complex summations and if there is no such simplification they will prove this to be the case. The authors present the underlying mathematical theory, and the principle theorems and proofs.
by D. P. Williamson, D. B. Shmoys - Cambridge University Press
This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. It is organized around techniques for designing approximation algorithms, including greedy and local search algorithms.
by Herbert Edelsbrunner - Duke University
The main topics to be covered in this course are: Design Techniques; Searching; Prioritizing; Graph Algorithms; Topological Algorithms; Geometric Algorithms; NP-completeness. The emphasis will be on algorithm design and on algorithm analysis.
by Wojciech Szpankowski - Wiley-Interscience
A book on a topic that has witnessed a surge of interest over the last decade, owing in part to several novel applications in data compression and computational molecular biology. It describes methods employed in average case analysis of algorithms.