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Principles of Data Mining (Adaptive Computation and Machine Learning)

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The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

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The Mathematical Theory of Nonblocking Switching Networks (Series on Applied Mathematics, Volume 11)
The Mathematical Theory of Nonblocking Switching Networks (Series on Applied Mathematics, Volume 11)
The first edition of this book covered in depth the mathematical theory of nonblocking multistage interconnecting networks, which is applicable to both communication and computer networks. This comprehensively updated version puts more emphasis to the multicast and multirate networks which are under fast development recently due to their wide...
An Introduction to Neural Networks
An Introduction to Neural Networks
Covers: artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps.

This book grew out of a set of course notes for a neural networks module given as part of a
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Grid Application Systems Design
Grid Application Systems Design
Grid computing is an emerging technology designed for high-powered applications. Grid Application Systems Design shows how to unleash the high performance of Grid technology. It begins by delving into the history and theory of grid computing, providing background on the concepts, terminology, and issues surrounding it. The book then examines...

Graph Transformations and Model-Driven Engineering
Graph Transformations and Model-Driven Engineering

This festschrift volume, published in honor of Manfred Nagl on the occasion of his 65th birthday, contains 30 refereed contributions, edited by five of his academic descendants. The types of papers vary significantly, including classic research papers in the style of journal articles, surveys of focused research areas, essays reflecting on...

Guide to Assembly Language: A Concise Introduction
Guide to Assembly Language: A Concise Introduction
The purpose of this text is to assist one in learning how to program in Intel assembly language in a minimal amount of time. In addition, through programming the reader learns more about the computer architecture of the Intel 32-bit processor and also the relationship between high-level languages and low-level languages.

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Microelectronics - Systems and Devices
Microelectronics - Systems and Devices

This is a completely new textbook written to be fully in line with the new BTEC Higher National unit from Edexcel, the 2000 specification Advanced GNVQ unit, BTEC NII and NIII, and A-Level modules. The resulting breadth of coverage makes Microelectronics - Systems and Devices an excellent international student text.
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