Data Mining Concepts And Techniques By Jiawei Han Micheline Kamber Pdf
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Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data KDD.
- Data Mining: Concepts and Techniques,
- Data Mining Concepts And Techniques Jiawei Han Micheline Kamber (2000) pdf
- Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
- Data Mining Concepts and Techniques by Han Jiawei Kamber Micheline
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Data Mining: Concepts and Techniques,
Our capabilities of both generating and collecting data have been increasing rapidly in the last several decades. Contributing factors include the widespread use of bar codes for most commercial products, the computerization of many business, scientic and government transactions and managements, and advances in data collection tools ranging from scanned texture and image platforms, to on-line instrumentation in manufacturing and shopping, and to satellite remote sensing systems.
In addition, popular use of the World Wide Web as a global information system has ooded us with a tremendous amount of data and information.
This explosive growth in stored data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amounts of data into useful information and knowledge.
This book explores the concepts and techniques ofdata mining, a promising and ourishing frontier in database systems and new database applications. Data mining, also popularly referred to asknowledge discovery in databases KDD , is the automated or convenient extraction of patterns representing knowledge implicitly stored in large databases, data warehouses, and other massive information repositories. Data mining is a multidisciplinary eld, drawing work from areas including database technology, articial in-telligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing, and data visualization.
We present the material in this book from adatabase perspective. That is, we focus on issues relating to the feasibility, usefulness, eciency, and scalability of techniques for the discovery of patterns hiddenin large databases. As a result, this book is not intended as an introduction to database systems, machine learning, or statistics, etc. Rather, the book is a comprehensive introduction to data mining, presented with database issues in focus.
It should be useful for computing science students, application developers, and business professionals, as well as researchers involved in any of the disciplines listed above. Data mining emerged during the late 's, has made great strides during the 's, and is expected to continue to ourish into the new millennium.
This book presents an overall picture of the eld from a database researcher's point of view, introducing interesting data mining techniques and systems, and discussing applications and research directions. An important motivation for writing this book was the need to build an organized framework for the study of data mining a challenging task owing to the extensive multidisciplinary nature of this fast developing eld.
We hope that this book will encourage people with dierent backgrounds and experiences to exchange their views regarding data mining so as to contribute towards the further promotion and shaping of this exciting and dynamic eld. This book is designed to give a broad, yet in depth overview of the eld of data mining.
You will nd it useful for teaching a course on data mining at an advanced undergraduate level, or the rst-year graduate level. In addition, individual chapters may be included as material for courses on selected topics in database systems or in articial intelligence.
We have tried to make the chapters as self-contained as possible. For a course taught at the undergraduate level, you might use chapters 1 to 8 as the core course material. Remaining class material may be selected from among the more advanced topics described in chapters 9 and For a graduate level course, you may choose to cover the entire book in one semester.
We hope that this textbook will spark your interest in the fresh, yet evolving eld of data mining. We have attempted to present the material in a clear manner, with careful explanation of the topics covered. Each chapter ends with a summary describing the main points. Although this book was designed as a textbook, we have tried to organize it so that it will also be useful to you as a reference book or handbook, should you later decide to pursue a career in data mining.
You should have some knowledge of the concepts and terminology associated with database systems. You should have some knowledge of database querying, although knowledge of any specic query language is not required. You should have some programming experience. In particular, you should be able to read pseudo-code, and. It will be helpful to have some preliminary background in statistics, machine learning, or pattern recognition. However, we will familiarize you with the basic concepts of these areas that are relevant to data mining from a database perspective.
This book was designed to cover a broad range of topics in the eld of data mining. As a result, it is a good handbook on the subject. Because each chapter is designed to be as stand-alone as possible, you can focus on the topics that most interest you. Much of the book is suited to applications programmers or information service managers like yourself who wish to learn about the key ideas of data mining on their own.
The techniques and algorithms presented are of practical utility. In Chapter 10, we briey discuss data mining systems in commercial use, as well as promising research prototypes. Each algorithm presented in the book is illustrated in code. If you wish to implement any of the algorithms, you should nd the translation of our pseudo-code into the programming language of your choice to be a fairly straightforward task. Chapter 1 provides an introduction to the multidisciplinaryeld of data mining.
It discusses the evolutionary path of database technology which led up to the need for data mining, and the importance of its application potential. The basic architecture of data mining systems is described, and a brief introduction to the concepts of database systems and data warehouses is given. A detailed classication of data mining tasks is presented, based on the dierent kinds of knowledge to be mined.
A classication of data mining systems is presented, and major challenges in the eld are discussed. Topics include the concept of data warehouses and multidimensional databases, the construction of data cubes, the implementation of on-line analytical processing, and the relationship between data warehousing and data mining.
Chapter 4 introduces the primitives of data mining which dene the specication of a data mining task. It describes a data mining query language DMQL , and provides examples of data mining queries. Other topics include the construction of graphical user interfaces, and the specication and manipulation of concept hierarchies. Chapter 5 describes techniques for concept description, including characterization and discrimination.
An attribute-oriented generalization technique is introduced, as well as its dierent implementations including a gener-alized relation technique and a multidimensional data cube technique. Several forms of knowledge presentation and visualization are illustrated. Relevance analysis is discussed.
Methods for class comparison at multiple abstraction levels, and methods for the extraction of characteristic rules and discriminant rules with interestingness measurements are presented. In addition, statistical measures for descriptive mining are discussed. Chapter 6 presents methods for mining association rules in transaction databases as well as relational databases and data warehouses. It includes a classication of association rules, a presentation of the basic Apriori algorithm and its variations, and techniques for mining multiple-level association rules, multidimensional association rules, quantitative association rules, and correlation rules.
Strategies for nding interesting rules by constraint-based mining and the use of interestingness measures to focus the rule search are also described. Chapter 7 describes methods for data classication and predictive modeling. Major methods of classication and prediction are explained, including decision tree induction, Bayesian classication, the neural network technique of backpropagation, k-nearest neighbor classiers, case-based reasoning, genetic algorithms, rough set theory, and fuzzy set approaches.
Association-based classication, which applies association rule mining to the problem of classication, is presented. Methods of regression are introduced, and issues regarding classier accuracy are discussed.
Chapter 8 describes methods of clustering analysis. It rst introduces the concept of data clustering and then presents several major data clustering approaches, including partition-based clustering, hierarchical clustering, and model-based clustering. Methods for clustering continuous data, discrete data, and data in multidimensional data cubes are presented.
The scalability of clustering algorithms is discussed in detail. Chapter 9 discusses methods for data mining in advanced database systems. It includes data mining in object-oriented databases, spatial databases, text databases, multimedia databases, active databases, temporal databases, heterogeneous and legacy databases, and resource and knowledge discovery in the Internet information base.
Finally, in Chapter 10, we summarize the concepts presented in this book and discuss applications of data mining and some challenging research issues.
It is likely that this book may contain typos, errors, or omissions. If you notice any errors, have suggestions regarding additional exercises or have other constructive criticism, we would be very happy to hear from you.
We welcome and appreciate your suggestions. You can send your comments to:. Alternatively, you can use electronic mails to submit bug reports, request a list of known errors, or make con-structive suggestions. To receive instructions, send email todk cs. We regret that we cannot personally respond to all e-mails. We would like to express our sincere thanks to all the members of the data mining research group who have been working with us at Simon Fraser University on data mining related research, and to all the members of theDBMiner.
TheDBMinerdevelopment team currently consists of the following active. This book is an introduction to what has come to be known asdata miningandknowledge discovery in databases. The material in this book is presented from a database perspective, where emphasis is placed on basic data mining concepts and techniques for uncovering interesting data patterns hidden in large data sets. The implementation methods discussed are particularly oriented towards the development ofscalableandecientdata mining tools.
In this chapter, you will learn how data mining is part of the natural evolution of database technology, why data mining is important, and how it is dened. You will learn about the general architecture of data mining systems, as well as gain insight into the kinds of data on which mining can be performed, the types of patterns that can be found, and how to tell which patterns represent useful knowledge. In addition to studying a classication of data mining systems, you will read about challenging research issues for building data mining tools of the future.
The major reason that data mining has attracted a great deal of attention in information industry in recent years is due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge.
The information and knowledge gained can be used for applications ranging from business management, production control, and market analysis, to engineering design and science exploration. Data mining can be viewed as a result of the natural evolution of information technology. An evolutionary path has been witnessed in the database industry in the development of the following functionalities Figure 1. For instance, the early development of data collection and database creation mechanisms served as a prerequisite for later development of eective mechanisms for data storage and retrieval, and query and transaction processing.
With numerous database systems oering query and transaction processing as common practice, data analysis and understanding has naturally become the next target. Since the 's, database and information technology has been evolving systematically from primitive le pro-cessing systems to sophisticated and powerful databases systems.
The research and development in database systems since the 's has led to the development of relational database systems where data are stored in relational table structures; see Section 1. In addition, users gained convenient and exible data access through query languages, query processing, and user interfaces.
Ecient methods for on-line transaction processing OLTP , where a query is viewed as a read-only transaction, have contributed substantially to the evolution and wide acceptance of relational technology as a major tool for ecient storage, retrieval, and management of large amounts of data.
Database technology since the mids has been characterized by the popular adoption of relational technology and an upsurge of research and development activities on new and powerful database systems. These employ. Issues related to the distribution, diversication, and sharing of data have been studied extensively. Heterogeneous database systems and Internet-based global information systems such as the World-Wide Web WWW also emerged and play a vital role in the information industry.
Data Mining Concepts And Techniques Jiawei Han Micheline Kamber (2000) pdf
Our capabilities of both generating and collecting data have been increasing rapidly in the last several decades. Contributing factors include the widespread use of bar codes for most commercial products, the computerization of many business, scientic and government transactions and managements, and advances in data collection tools ranging from scanned texture and image platforms, to on-line instrumentation in manufacturing and shopping, and to satellite remote sensing systems. In addition, popular use of the World Wide Web as a global information system has ooded us with a tremendous amount of data and information. This explosive growth in stored data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amounts of data into useful information and knowledge. This book explores the concepts and techniques ofdata mining, a promising and ourishing frontier in database systems and new database applications.
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Jiawei Han and Micheline Kamber have been leading contributors to data mining research. knowledge. This book explores the concepts and techniques of data mining, a promising and Table of contents of the book in PDF. Errata on the.
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
A distribution with more than one mode is said to be bimodal, trimodal, etc. Management Systems. Advanced Frequent Pattern Mining Chapter 8. Clustering Validity, Minimum Introduction. To develop skills of using recent data mining software for solving practical problems.
Book annotation not available for this title. The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. The text is supported by a strong outline.
Data Mining Concepts and Techniques by Han Jiawei Kamber Micheline
За ее спиной ТРАНСТЕКСТ издал предсмертный оглушающий стон. Когда распался последний силиконовый чип, громадная раскаленная лава вырвалась наружу, пробив верхнюю крышку и выбросив на двадцать метров вверх тучу керамических осколков, и в то же мгновение насыщенный кислородом воздух шифровалки втянуло в образовавшийся вакуум. Сьюзан едва успела взбежать на верхнюю площадку лестницы и вцепиться в перила, когда ее ударил мощный порыв горячего ветра. Повернувшись, она увидела заместителя оперативного директора АНБ; он стоял возле ТРАНСТЕКСТА, не сводя с нее глаз. Вокруг него бушевала настоящая буря, но в его глазах она увидела смирение.
Стратмор опустился на колени и повернул тяжелый винтовой замок. Теперь крышку не поднять изнутри. Подсобка компьютера надежно закрыта. Ни он, ни Сьюзан не услышали тихих шагов в направлении Третьего узла. ГЛАВА 60 По зеркальному коридору Двухцветный отправился с наружной террасы в танцевальный зал. Остановившись, чтобы посмотреть на свое отражение в зеркале, он почувствовал, что за спиной у него возникла какая-то фигура.
Спутница? - бессмысленно повторил Беккер. - Проститутка, что. Клушар поморщился: - Вот. Если вам угодно использовать это вульгарное слово. - Но… офицер ничего не сказал о… - Разумеется.
- Слова лились потоком, словно ждали много лет, чтобы сорваться с его губ. - Я люблю. Я люблю .
У правительств должно быть право собирать информацию, в которой может содержаться угроза общественной безопасности. - Господи Иисусе! - шумно вздохнул Хейл. - Похоже, Стратмор здорово промыл тебе мозги. Ты отлично знаешь, что ФБР не может прослушивать телефонные разговоры произвольно: для этого они должны получить ордер.
Его голос гремел: - Три. Разница между 238 и 235 - три. Все подняли головы. - Три! - крикнула Сьюзан, перекрывая оглушающую какофонию сирен и чьих-то голосов.
Затаив дыхание, она вглядывалась в экран. КОД ОШИБКИ 22 Сьюзан вздохнула с облегчением. Это была хорошая весть: проверка показала код ошибки, и это означало, что Следопыт исправен. Вероятно, он отключился в результате какой-то внешней аномалии, которая не должна повториться.
Беккер покачал головой: - Отнюдь. Тут написано - Quis custodiet ipsos custodes. Это можно примерно перевести как… - Кто будет охранять охранников! - закончила за него Сьюзан. Беккера поразила ее реакция.
Хм-м, - наконец произнесла. - Вчерашняя статистика безукоризненна: вскрыто двести тридцать семь кодов, средняя стоимость - восемьсот семьдесят четыре доллара.