Data Mining The Textbook

Data Mining The Textbook 是数据挖掘很好的教材资料课程，虽然是英文版，但是还是能够深入
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Data Mining: The Textbook
20190203Data Mining: The Textbook By 作者: Charu C. Aggarwal ISBN10 书号: 3319141414 ISBN13 书号: 9783319141411 Edition 版本: 2015 出版日期: 20150414 pages 页数: (734 ) $89.99 This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing
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Data Mining: The Textbook (Springer 2015原版超清)
20190426This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, timeseries data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor.
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Data Mining  The Textbook.pdf
20190703Data Mining  The Textbook（英文原版，Charu C. Aggarwal, 2005） 数据挖掘教科书，Charu C. Aggarwal编著
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彩屏的资料
20140713TFT资料，彩屏原理图，可以快速应用与单片机
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Data.Mining.for.Business.Analytics.Concepts.Techniques.and.Applications.in.R
20171204Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and opensource software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: Two new coauthors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described Endofchapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upperundergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “ This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neur
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Statistics Data Mining and Machine Learning in Astronomy 无水印pdf
20170927Statistics Data Mining and Machine Learning in Astronomy 英文无水印pdf pdf所有页面使用FoxitReader和PDFXChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除
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Principles of data mining
20180403This book is designed to be suitable for an introductory course at either un dergraduate or masters level. It can be used as a textbook for a taught unit in a degree programme on potentially any of a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioin formatics and Forensic Science. It is also suitable for use as a selfstudy book for those in technical or management positions who wish to gain an understanding of the subject that goes beyond the superficial. It goes well beyond the gener alities of many introductory books on Data Mining but—unlike many other books — you will not need a degree and/or considerable fluency in Mathematics to understand it.
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数据挖掘与分析：基本概念与算法 英文版 DATA MINING AND ANALYSIS Fundamental Concepts and Algorithms
20180615The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet indepth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.
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Data.Mining.for.Business.Analytics.with.JMP.Pro
20161019Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring handson applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, realworld examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter Endofchapter examples and exercises that allow readers to expand their comprehension of the presented material Datarich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduatelevel courses on data mining, predictive analytics, and business analytics. The book is also a oneofakind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other datarich field. Table of Contents Part I: Preliminaries Chapter 1 Introduction Chapter 2 Overview of the Data Mining Process Part II: Data Exploration and Dimension Reduction Chapter 3 Data Visualization Chapter 4 Dimension Reduction Part III: Performance Evaluation Chapter 5 Evaluating Predictive Performance Part IV: Prediction And Classification Methods Chapter 6 Multiple Linear Regression Chapter 7 kNearest Neighbors (kNN) Chapter 8 The Naive Bayes Classifier Chapter 9 Classification and Regression Trees Chapter 10 Logistic Regression Chapter 11 Neural Nets Chapter 12 Discriminant Analysis Chapter 13 Combining Methods: Ensembles and Uplift Modeling Part V: Mining Relationships among Records Chapter 14 Cluster Analysis Part VI: Forecasting Time Series Chapter 15 Handling Time Series Chapter 16 RegressionBased Forecasting Chapter 17 Smoothing Methods Part VII: Cases Chapter 18 Cases
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Data Mining Text Book
20180718数据挖掘经典教材,本教科书探讨了数据挖掘从基本原理到复杂数据类型及其应用的不同方面，捕捉了数据挖掘问题领域的广泛多样性。它超越了传统的数据挖掘问题，引入了先进的数据类型，如文本、时间序列、离散序列、空间数据、图形数据和社交网络。迄今为止，还没有一本书以综合和综合的方式解决所有这些问题。
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DATA MINING TEXTBOOK
20121018数据库课本英文版可以下载,只要2个分,希望大家喜欢,欢迎下载.
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data mining textbook chapter 1
20130722一个关于数据库挖掘书的章节
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Feature Extraction 等5本
20180412Data Mining The Textbook.epub Feature Extraction.pdf Neural Networks Tricks of the Trade.epub Outlier Analysis.pdf Recommender Systems The Textbook.epub
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data mining
20140712data mining textbook
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Principles and Theory for Data Mining and Machine Learning
20100310This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons. Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an exploratory nature. There are numerous computed examples, complete with code, so that further computations can be carried out readily. The book also serves as a handbook for researchers who want a conceptual overview of the central topics in data mining and machine learning. Bertrand Clarke is a Professor of Statistics in the Department of Medicine, Department of Epidemiology and Public Health, and the Center for Computational Sciences at the University of Miami. He has been on the Editorial Board of the Journal of the American Statistical Association, the Journal of Statistical Planning and Inference, and Statistical Papers. He is cowinner, with Andrew Barron, of the 1990 Browder J. Thompson Prize from the Institute of Electrical and Electronic Engineers. Ernest Fokoue is an Assistant Professor of Statistics at Kettering University. He has also taught at Ohio State University and been a long term visitor at the Statistical and Mathematical Sciences Institute where he was a Postdoctoral Research Fellow in the Data Mining and Machine Learning Program. In 2000, he was the winner of the Young Researcher Award from the International Association for Statistical Computing. Hao Helen Zhang is an Associate Professor of Statistics in the Department of Statistics at North Carolina State University. For 20032004, she was a Research Fellow at SAMSI and in 2007, she won a Faculty Early Career Development Award from the National Science Foundation. She is on the Editorial Board of the Journal of the American Statistical Association and Biometrics.
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Practical Graph Mining with R(CRC,2013)
20160227Discover Novel and Insightful Knowledge from Data Represented as a Graph Practical Graph Mining with R presents a “doityourself” approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs. HandsOn Application of Graph Data Mining Each chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve realworld problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and proteinprotein interaction networks, and community detection in social networks. Develops Intuition through EasytoFollow Examples and Rigorous Mathematical Foundations Every algorithm and example is accompanied with R code. This allows readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice. The text also gives a rigorous, formal explanation of the underlying mathematics of each technique. Makes Graph Mining Accessible to Various Levels of Expertise Assuming no prior knowledge of mathematics or data mining, this selfcontained book is accessible to students, researchers, and practitioners of graph data mining. It is suitable as a primary textbook for graph mining or as a supplement to a standard data mining course. It can also be used as a reference for researchers in computer, information, and computational science as well as a handy guide for data analytics practitioners.

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