Title: Data Mining: Theories, Algorithms, and Examples Author: Nong Ye Length: 349 pages Edition: 1 Language: English Publisher: CRC Press Publication Date: 2013-07-26 ISBN-10: 1439808384 ISBN-13: 9781439808382 New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them. Table of Contents Part I: An Overview of Data Mining Chapter 1: Introduction to Data, Data Patterns, and Data Mining Part II: Algorithms for Mining Classification and Prediction Patterns Chapter 2: Linear and Nonlinear Regression Models Chapter 3: Naïve Bayes Classifier Chapter 4: Decision and Regression Trees Chapter 5: Artificial Neural Networks for Classification and Prediction
Ideal for graduate and senior undergraduate courses in computer arithmetic and advanced digital design, Computer Arithmetic: Algorithms and Hardware Designs, Second Edition, provides a balanced, comprehensive treatment of computer arithmetic. It covers topics in arithmetic unit design and circuit implementation that complement the architectural and algorithmic speedup techniques used in high-performance computer architecture and parallel processing. Using a unified and consistent framework, the text begins with number representation and proceeds through basic arithmetic operations, floating-point arithmetic, and function evaluation methods. Later chapters cover broad design and implementation topics-including techniques for high-throughput, low-power, fault-tolerant, and reconfigurable arithmetic. An appendix provides a historical view of the field and speculates on its future. An indispensable resource for instruction, professional development, and research, Computer Arithmetic: Algorithms and Hardware Designs, Second Edition, combines broad coverage of the underlying theories of computer arithmetic with numerous examples of practical designs, worked-out examples, and a large collection of meaningful problems. This second edition includes a new chapter on reconfigurable arithmetic, in order to address the fact that arithmetic functions are increasingly being implemented on field-programmable gate arrays (FPGAs) and FPGA-like configurable devices. Updated and thoroughly revised, the book offers new and expanded coverage of saturating adders and multipliers, truncated multipliers, fused multiply-add units, overlapped quotient digit selection, bipartite and multipartite tables, reversible logic, dot notation, modular arithmetic, Montgomery modular reduction, division by constants, IEEE floating-point standard formats, and interval arithmetic. Features: * Divided into 28 lecture-size chapters * Emphasizes both the underlying theories of computer arithmetic and actual hardware designs * Carefully links computer arithmetic to other subfields of computer engineering * Includes 717 end-of-chapter problems ranging in complexity from simple exercises to mini-projects * Incorporates many examples of practical designs * Uses consistent standardized notation throughout * Instructor's manual includes solutions to text problems * An author-maintained website http://www.ece.ucsb.edu/~parhami/text_comp_arit.htm contains instructor resources, including complete lecture slides Table of Contents PART I NUMBER REPRESENTATION Chapter 1 Numbers and Arithmetic Chapter 2 Representing Signed Numbers Chapter 3 Redundant Number Systems Chapter 4 Residue Number Systems PART II ADDITION/SUBTRACTION Chapter 5 Basic Addition and Counting Chapter 6 Carry-Lookahead Adders Chapter 7 Variations in Fast Adders Chapter 8 Multioperand Addition PART III MULTIPLICATION Chapter 9 Basic Multiplication Schemes Chapter 10 High-Radix Multipliers Chapter 11 Tree and Array Multipliers Chapter 12 Variations in Multipliers PART IV DIVISION Chapter 13 Basic Division Schemes Chapter 14 High-Radix Dividers Chapter 15 Variations in Dividers Chapter 16 Division by Convergence PART V REAL ARITHMETIC Chapter 17 Floating-Point Representations Chapter 18 Floating-Point Operations Chapter 19 Errors and Error Control Chapter 20 Precise and Certifiable Arithmetic PART VI FUNCTION EVALUATION Chapter 21 Square-Rooting Methods Chapter 22 The CORDIC Algorithms Chapter 23 Variations in Function Evaluation Chapter 24 Arithmetic by Table Lookup PART VII IMPLEMENTATION TOPICS Chapter 25 High-Throughput Arithmetic Chapter 26 Low-Power Arithmetic Chapter 27 Fault-Tolerant Arithmetic Chapter 28 Reconfigurable Arithmetic Appendix: Past, Present, and Future
Mission ISSN:2327-1981 EISSN:2327-199X With the large amounts of information available to organizations in today’s digital world, there is a need for continual research surrounding emerging methods and tools for collecting, analyzing, and storing data. The Advances in Data Mining & Database Management (ADMDM) series aims to bring together research in information retrieval, data analysis, data warehousing, and related areas in order to become an ideal resource for those working and studying in these fields. IT professionals, software engineers, academicians and upper-level students will find titles within the ADMDM book series particularly useful for staying up-to-date on emerging research, theories, and applications in the fields of data mining and database management.
Data Mining and Knowledge Discovery Handbook2010-09-17
* Covers over 25 new topics, as well as most updated information on topics presented in first edition Includes over 30 new world wide contributors, who are experts in this field New case studies introduced based on real world examples * Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This handbook first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook, 2nd Edition is designed for research scientists, libraries and advanced-level students in computer science and engineering as a reference. This handbook is also suitable for professionals in industry, for computing applications, information systems management, and strategic research management. Content Level » Research Keywords » Bayesian networks - KDD - algorithm - data mining - data mining applications - decision trees - ensemble method - knowledge discovery - large datasets - preprocessing method - soft computing method - statistical method - text mining - web mining
Computer Age Statistical Inference - Algorithms, Evidence and Data Science2017-09-08
大数据时代的统计学 This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories – Bayesian, frequentist, Fisherian – individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
This book is the result of very interesting discussions, debates and dialogues with a large number of people at various levels of seniority, working at startups as well as long-established businesses, and in a variety of industries, from science to media to finance. The book is intended to be a companion to data analysts and budding data scientists that have some working experience with both programming and statistical modelling, but who have not necessarily delved into the wonders of data analytics and machine learning. The book uses Python1 as a tool to implement and exploit some of the most common algorithms used in data science and data analytics today.
Text Mining Concepts, Implementation, and Big Data Challenge2018-06-10
This book is concerned with the concept, the theories, and the implementations of text mining. In Part I, we provide the fundamental knowledge about the text mining tasks, such as text preprocessing and text association. In Parts II and III, we describe the representative approaches to text mining tasks, provide guides for implementing the text mining systems by presenting the source codes in Java, and explain the schemes of evaluating them. In Part IV, we cover other text mining tasks, such as text summarization and egmentation, and the composite text mining tasks. Overall, in this book, we explain the text mining tasks in the functional view, describe the main approaches, and provide guidance for implementing systems, mainly about text categorization and clustering.
Machine Learning, Optimization, and Big Data(2017).pdf2018-06-28
Machine Learning, Optimization, and Big Data(2017).pdf
Ensemble Methods Foundations and Algorithms(清晰版)2013-01-01
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement
Algorithms and Theory of Computation Handbook, Second Edition: Special Topics and Techniques provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems. Along with updating and revising many of the existing chapters, this second edition contains more than 15 new chapters. This edition now covers self-stabilizing and pricing algorithms as well as the theories of privacy and anonymity, databases, computational games, and communication networks. It also discusses computational topology, natural language processing, and grid computing and explores applications in intensity-modulated radiation therapy, voting, DNA research, systems biology, and financial derivatives. This best-selling handbook continues to help computer professionals and engineers find significant information on various algorithmic topics. The expert contributors clearly define the terminology, present basic results and techniques, and offer a number of current references to the in-depth literature. They also provide a glimpse of the major research issues concerning the relevant topics.
Unifying Theories of Programming - C.A.R. Hoare and He Jifeng, 1998.pdf2014-05-25
Unifying Theories of Programming (UTP) deals with program semantics. It shows how denotational semantics, operational semantics and algebraic semantics can be combined in a unified framework for the formal specification, design and implementation of programs and computer systems.
Everyone encounters statistics on a daily basis. They are used in proposals, reports, requests, and advertisements, among others, to support assertions, opinions, and theories. Unless you’re a trained statistician, it can be bewildering. What are the numbers really saying or not saying? Better Business Decisions from Data: Statistical Analysis for Professional Success provides the answers to these questions and more. It will show you how to use statistical data to improve small, every-day management judgments as well as major business decisions with potentially serious consequences. Author Peter Kenny—with deep experience in industry—believes that "while the methods of statistics can be complicated, the meaning of statistics is not." He first outlines the ways in which we are frequently misled by statistical results, either because of our lack of understanding or because we are being misled intentionally. Then he offers sound approaches for understanding and assessing statistical data to make excellent decisions. Kenny assumes no prior knowledge of statistical techniques; he explains concepts simply and shows how the tools are used in various business situations. With the arrival of Big Data, statistical processing has taken on a new level of importance. Kenny lays a foundation for understanding the importance and value of Big Data, and then he shows how mined data can help you see your business in a new light and uncover opportunity. Among other things, this book covers: How statistics can help you assess the probability of a successful outcome How data is collected, sampled, and best interpreted How to make effective forecasts based on the data at hand How to spot the misuse or abuse of statistical evidence in advertisements, reports, and proposals How to commission a statistical analysis Arranged in seven parts—Uncertainties, Data, Samples, Comparisons, Relationships, Forecasts, and Big Data—Better Business Decisions from Data is a guide for busy people in general management, finance, marketing, operations, and other business disciplines who run across statistics on a daily or weekly basis. You’ll return to it again and again as new challenges emerge, making better decisions each time that boost your organization’s fortunes—as well as your own. What youll learn How raw data are processed to obtain information, with known reliability, for the basis of decision making. What a statistical analysis can--and can't--do. Why certainty is illusive and how we can be misled by statistical results. The basics of probability, sampling, reliability, regression, distribution and other statistical techniques essential for decision making in all aspects of business. How to commission data gathering and processing in advance of big decisions Who this book is for The primary audience includes managers and professionals in business and industry who need to understand statistics to make or approve decisions, or to commission statistical investigations and assess their results. It's also for those who want to understand how statistics can be used to mislead or shroud the true facts. A secondary audience consists of students of disciplines that require some knowledge of statistics—economics, finance, political science, physics, biology, and more—as well as general readers who simply wish to have a more informed view of the daily dose of statistics offered up by news organizations, advocacy groups, and the government, among others. Table of Contents Part I: Uncertainties Chapter 1: The Scarcity of Certainty Chapter 2: Sources of Uncertainty Chapter 3: Probability Part II: Data Chapter 4: Sampling Chapter 5: The Raw Data Part III: SamplesThe Chapter 6: Descriptive Data Chapter 7: Numerical Data Part IV: Comparisons Chapter 8: Levels of Significance Chapter 9: General Procedure for Comparisons Chapter 10: Comparisons with Numerical Data Chapter 11: Comparisons with Descriptive Data Chapter 12: Types of Error Part V: Relationships Chapter 13: Cause and Effect Chapter 14: Relationships with Numerical Data Chapter 15: Relationships with Descriptive Data Chapter 16: Multivariate Data Part VI: Forecasts Chapter 17: Extrapolation Chapter 18: Forecasting from Known Distributions Chapter 19: Time Series Chapter 20: Control Charts Chapter 21: Reliability Part VII: Big Data Chapter 22: Data Mining Chapter 23: Predictive Analytics Chapter 24: Getting Involved with Big Data Chapter 25: Concerns with Big Data Chapter 26: References and Further Reading
Truesdell C. Noll W. The non-linear field theories of mechanics2012-01-11
Truesdell C., Noll W. The non-linear field theories of mechanics (3ed., Springer, 2004)(ISBN 3540027793)(600dpi)(K)(T)(627s)
Swarm Intelligence Volume 2：Innovation, new algorithms and methods by Ying Tan2019-02-19
Swarm Intelligence Volume 2: Innovation, new algorithms and methods by Ying Tan English | 2018 | ISBN: 1785616297 | 544 Pages | PDF | 51 MB Swarm Intelligence (SI) is one of the most important and challenging paradigms under the umbrella of computational intelligence. It focuses on the research of collective behaviours of a swarm in nature and/or social phenomenon to solve complicated and difficult problems which cannot be handled by traditional approaches. Thousands of papers are published each year presenting new algorithms, new improvements and numerous real world applications. This makes it hard for researchers and students to share their ideas with other colleagues; follow up the works from other researchers with common interests; and to follow new developments and innovative approaches. This complete and timely collection fills this gap by presenting the latest research systematically and thoroughly to provide readers with a full view of the field of swarm. Students will learn the principles and theories of typical swarm intelligence algorithms; scholars will be inspired with promising research directions; and practitioners will find suitable methods for their applications of interest along with useful instructions. Volume 2 includes 17 chapters covering front-edge research with novel and newly proposed algorithms and methods. With contributions from an international selection of leading researchers, Swarm Intelligence is essential reading for engineers, researchers, professionals and practitioners with interests in swarm intelligence.
Theories of Consciousness_ An Introduction and Assessment, 2nd Edition2018-03-01
Despite recent strides in neuroscience and psychology that have deepened understanding of the brain, consciousness remains one of the greatest philosophical and scientific puzzles. The second edition of Theories of Consciousness: An Introduction and Assessment provides a fresh and up-to-date introduction to a variety of approaches to consciousness, and contributes to the current lively debate about the nature of consciousness and whether a scientific understanding of it is possible. After an initial overview of the status and prospects of physicalism in the face of the problem of consciousness, William Seager explores key themes from Descartes - the founder of the modern problem of consciousness. He then turns to the most important theories of consciousness: identity theories and the generation problem higher-order thought theories of consciousness self-representational theories of consciousness Daniel Dennett’s theory of consciousness attention-based theories of consciousness representational theories of consciousness conscious intentionality panpsychism neutral monism. Thoroughly revised and expanded throughout, this second edition includes new chapters on animal consciousness, reflexive consciousness, combinatorial forms of panpsychism and neutral monism, as well as a significant new chapter on physicalism, emergence and consciousness. The book’s broad scope, depth of coverage and focus on key philosophical positions and arguments make it an indispensable text for those teaching or studying philosophy of mind and psychology. It is also an excellent resource for those working in related fields such as cognitive science and the neuroscience of consciousness.
解压密码 share.weimo.info-英文原版-Probability and Statistics for Engineering and the Sciences 9th Edition2019-09-23
解压密码 share.weimo.info Put statistical theories into practice with PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9th . Always a market favorite, this calculus-based book offers a comprehensive introduction to probability and statistics while demonstrating how to apply concepts, models, and methodologies in today's engineering and scientific workplaces. Jay Devore, an award-winning professor and internationally recognized author and statistician, stresses lively examples and engineering activities to drive home the numbers without exhaustive mathematical development and derivations. Many examples, practice problems, sample tests, and simulations based on real data and issues help you build a more intuitive connection to the material. A proven and accurate book, PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9th also includes graphics and screen shots from SAS, MINITAB, and Java™ Applets to give you a solid perspective of statistics in action.,解压密码 share.weimo.info
Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings (Proceedings in Adaptation, Learning and Optimization) Over the last two decades the field of Intelligent Systems delivered to human kind significant achievements, while
computer age statistical inference2017-11-24
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science., Clarifies both traditional methods and current, popular algorithms (e.g. neural nets, random forests), Written by two world-leading researchers, Addressed to all fields that work with data