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A gripping first-person account of how scientists came to understand our universe's mysterious structureJ. Richard Gott was among the first cosmologists to propose that the structure of our universe is like a sponge made up of clusters of galaxies intricately connected by filaments of galaxies―a magnificent structure now called the "cosmic web" and mapped extensively by teams of astronomers. Here is his gripping insider's account of how a generation of undaunted theorists and observers solved the mystery of the architecture of our cosmos.The Cosmic Web begins with modern pioneers of extragalactic astronomy, such as Edwin Hubble and Fritz Zwicky. It goes on to describe how, during the Cold War, the American school of cosmology favored a model of the universe where galaxies resided in isolated clusters, whereas the Soviet school favored a honeycomb pattern of galaxies punctuated by giant, isolated voids. Gott tells the stories of how his own path to a solution began with a high-school science project when he was eighteen, and how he and astronomer Mario Jurič measured the Sloan Great Wall of Galaxies, a filament of galaxies that, at 1.37 billion light-years in length, is one of the largest structures in the universe.Drawing on Gott’s own experiences working at the frontiers of science with many of today’s leading cosmologists, The Cosmic Web shows how ambitious telescope surveys such as the Sloan Digital Sky Survey are transforming our understanding of the cosmos, and how the cosmic web holds vital clues to the origins of the universe and the next trillion years that lie ahead.

On megaparsec scales, matter and galaxies have aggregated into a complex network of interconnected filaments, wall-like structures and compact clusters surrounded by large near-empty void regions. Dubbed the 'Cosmic Web', theoretical and observational studies have led to its recognition as a key aspect of structure in the Universe, representing a universal phase in the gravitationally driven emergence and evolution of cosmic structure. IAU Symposium 308 marked the centenary of the birth of the Russian physicist and cosmologist Yakov B. Zeldovich (1914-87), who was instrumental in the development of this view of structure formation. His seminal work paved the way towards an understanding of the complex web-like structure observed in our Universe. This volume synthesizes the insights obtained from many different observational and theoretical studies, and helps prepare researchers and students working in this vibrant field for the many upcoming surveys.

On megaparsec scales, matter and galaxies have aggregated into a complex network of interconnected filaments, wall-like structures and compact clusters surrounded by large near-empty void regions. Dubbed the 'Cosmic Web', theoretical and observational studies have led to its recognition as a key aspect of structure in the Universe, representing a universal phase in the gravitationally driven emergence and evolution of cosmic structure. IAU Symposium 308 marked the centenary of the birth of the Russian physicist and cosmologist Yakov B. Zeldovich (1914-87), who was instrumental in the development of this view of structure formation. His seminal work paved the way towards an understanding of the complex web-like structure observed in our Universe. This volume synthesizes the insights obtained from many different observational and theoretical studies, and helps prepare researchers and students working in this vibrant field for the many upcoming surveys.

There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. 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.

There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. 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.