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This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website. |
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 Inside Microsoft SQL Server 7.0This book is not an introductory treatise on SQL Server, although it does include one introductory chapter (Chapter 2). In Chapter 1, I discuss the history of the product (which I lived), from SQL Server's inception and partnership with Sybase to its current success. But beyond these early chapters, the book is very detailed and written... | | Mobile TV: DVB-H, DMB, 3G Systems and Rich Media ApplicationsExclusively dedicated to Mobile TV, this book provides a detailed insight to mobile multimedia characterized efficient compression techniques, protocols formalized by 3GPP or 3GPP2, capabilities of broadcast, and mobile networks for delivering multimedia content. Network requirements such as spectrum; chipsets, software and handsets which enable... | | |
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