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Data Mining. Concepts and Techniques.: Concepts and Techniques (Morgan Kaufmann) (Morgan Kaufmann Series in Data Management Systems) [Englisch] [Gebundene Ausgabe]

Jiawei Han , Micheline Kamber
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Kurzbeschreibung

6. September 2000 Morgan Kaufmann Series in Data Management Systems
Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. "Data Mining: Concepts and Techniques" equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases. Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as 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. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success. "Data Mining: Concepts and Techniques" is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms. The classroom features that are available online include: instructor's manual - course slides (in PowerPoint) - course supplementary readings - sample assignments and course projects. It offers a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data. It is organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn. It presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects. It provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis. It addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields.

Produktinformation

  • Gebundene Ausgabe: 550 Seiten
  • Verlag: Morgan Kaufmann (6. September 2000)
  • Sprache: Englisch
  • ISBN-10: 1558604898
  • ISBN-13: 978-1558604896
  • Größe und/oder Gewicht: 23,8 x 19 x 3 cm
  • Durchschnittliche Kundenbewertung: 5.0 von 5 Sternen  Alle Rezensionen anzeigen (2 Kundenrezensionen)
  • Amazon Bestseller-Rang: Nr. 495.100 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
  • Komplettes Inhaltsverzeichnis ansehen

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Produktbeschreibungen

Pressestimmen

"many excellent features" -- Tony Jenkins,Vice Chairman, British Computer Society's, Data Management Specialist Group

Synopsis

Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. "Data Mining: Concepts and Techniques" equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases. Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as 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. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success. "Data Mining: Concepts and Techniques" is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms.

The classroom features that are available online include: instructor's manual - course slides (in PowerPoint) - course supplementary readings - sample assignments and course projects. It offers a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data. It is organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn. It presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects. It provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis. It addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields.


In diesem Buch (Mehr dazu)
Einleitungssatz
This book is an introduction to what come to known as data mining and knowledge spective, where emphasis is placed on basic data mining concepts and techniques for uncovering interesting data patterns hidden in large data sets. Lesen Sie die erste Seite
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16 von 16 Kunden fanden die folgende Rezension hilfreich
Von "cedrix"
Format:Gebundene Ausgabe
Endlich ein wunderbares Buch zu dem Thema Data Warehousing, Data Mining etc. Dem Autor gelingt es auch dem unerfahrenen Leser mit Leichtigkeit in die Thematik einzuführen. Erstklassig werden Begriffe ausführlich erläutert, präzise definiert und gegeneinander abgegrenzt. Die Leserzielgruppe ist natürlich vorwiegend das Fachpublikum. Es eignet sich insbesondere hervorragend als Lehrbuch für alle Wirtschaftsinformatiker (und fachverwandte Gebiete) zumal es für jedes Kapitel Zusammenfassungen gibt, die ein extrem einfaches lernen ermöglichen. Hinzukommt eine Übungs-Sektion mit Fragen zu den Kapiteln und, ebenfalls getrennt nach Kapiteln, eine ausführliche Literaturangabe für Querverweise.
Der Inhalt wird dem Titel vollends gerecht: Angefangen bei Einleitungen über den Sinn und Zweck des DataMining, benutzte Techniken, mögliche Analysen (beschreibend oder vorhersagend), bis zu angewandten Architekturen und Implementationen finden in dem Buch sehr anschaulich und ausreichend Platz. Jeder wichtige Aspekt wird in einem separaten Kapitel ausführlichst erläutert.
Positiv ist auch: Der Autor gibt nicht nur den aktuellen Stand der Technik an, sondern gibt stets noch Ausblicke für zukünftige Entwicklungen an und weist auf aktuell zu lösende Probleme hin.
Fazi: Ein rundum gelungens Buch für Profis und solche die es werden wollen.
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14 von 14 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Gelungene, umfassende Einführung 17. Juni 2004
Format:Gebundene Ausgabe
Bereits der Untertitel "Concepts and Techniques" charakterisiert das Buch von Herrn Han und Frau Kamber sehr zutreffend: Es geht um die grundlegenden Konzepte hinter Data-Mining und die Techniken ihrer Umsetzung.
Nach einer kurzen Einordnung in den operativen Kontext des Data-Warehousings konzentrieren sich die Autoren auf Data-Mining: Formale Klassifikations- und Prädiktionsaufgaben werden als Konzepte eingeführt und mit Techniken wie Entscheidungsbäumen, Bayes'schen Netzwerken, Nächste-Nachbarn-Suche oder Regressionsmodellen gelöst. Es spricht sehr für die Gewissenhaftigkeit der Darstellung, dass die Bewertung der Vorhersagegüte zumindest touchiert wird. Die Assoziationsanalyse wird mit ungewöhnlich viel Tiefgang dargestellt - auch Multilevel- und multidimensionale Assoziationsregeln werden behandelt - was durch die vielfältigen Veröffentlichungen von Prof. Han zu diesem Thema verständlich ist. Zur Clusteranalyse werden vor allem die vielfältigen unterschiedlichen Ansätze vorgestellt und illustriert. Die für die praktische Anwendung häufig entscheidenden Aspekte von Datenvorbereitung und Datenmodellierung werden zwar nicht tiefgreifend behandelt, aber dem Titel gemäß zumindest als Konzepte angesprochen. Eine Einführung in Data-Mining auf komplexen geographischen, multimedialen oder Web-Datentypen runden die Darstellung ab.
Professor Han hat einen starken akademischen Hintergrund; seine Forschungen und Publikationen zu Data-Mining reichen bis zum Anfang der 1990er Jahre zurück. Seine Darstellungen sind sauber, die Aussagen inhaltlich fundiert und die gelegentlichen vorkommenden Formeln korrekt.
Lesen Sie weiter... ›
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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)
Amazon.com: 3.6 von 5 Sternen  24 Rezensionen
86 von 89 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A good textbook on the technical aspects of data mining 8. September 2000
Von Krishnan Pillaipakkamnatt - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
There are a number of books on data mining. The vast majority of them are non-technical in the sense that they talk a great deal about how data mining is a glorious area, without ever getting into the nitty gritty of how data mining algorithms actually work. There are also a couple of technical textbooks on data mining that are nothing more than mistitled books on machine learning (yes, I know, the ML arena does contribute a lot towards data mining). This is the first true textbook on data mining algorithms and techniques. It covers a vast array of topics and does ample justice to the vast majority of them. In fact, it even covers semi-automated (OLAP) technologies for data mining. The book consistently uses data from a single (fictitious) organization to illustrate most concepts. This gives a strong sense of cohesion to can actually be very different techniques. One key aspect of the book is its question-and-answer format. The main arguments in favor of such a format are (1) it is a clean way introduce a new topic or concept (2) students love it when things are laid out for them. On the other hand, such an approach seems inappropriate for a graduate level text. This book is certain to become "the standard" data mining textbook.

Update (Dec 25, 2004): My opinion about this book has changed over time. I've left the 5-start rating in place, although my current rating for the book is 4 (or even 3.5) stars. The main reason is that I had to supplement most of the chapters in the book with the original research papers to give my students a more complete picture of data mining (in other words, the material can be a bit shallow).
24 von 26 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Best introduction I know 14. November 2004
Von wiredweird - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
It is very easy to collect huge volumes of data - social statistics, bank records, biological data, and more - but very hard to pull useful facts out of the heap. This book is about processing large volumes of data in ways that let simple descriptions emerge.

This is an introductory level book, aimed at someone with reasonably good programming skills. A little facility with statistics might help, but certainly isn't necessary. The book starts gently, with some very basic questions: what is data mining exactly, when there seem to be so many definitions for the term? What is a data warehouse, and how does it differ from a database? Next, the authors address the data itself in terms of quality, usability, and organization for efficient access. The central chapters, 4 thhrough 8, address various kinds of query specification, kinds of relationships to extract, correlations, clustering, and classification. None of the discussions is especially deep. All, however, are presented in pseudocode or simple math that can easily be translated into working code. The careful reader learns a few basic principles that work well in many contexts: entropy maximization, Bayesian analysis, and simple stats. It may be surprising to see how little of normal statistical analysis is used. I suspect the authors assume that stats-savvy readers will already know how to apply significance testing, and that stats-naive readers don't need the distraction. The last chapters discuss complex data, where the best structure for the data and the questions to be asked of it are not at all obvious, and tools and applications used in data mining.

The book is nicely laid out as a textbook, with an orderly summary, problem set, and bibliography at the end of each chapter. The bibliography is more than just a list of names and authors - it actually helps the reader decide which references will give the best description of each of the chapter's topics.

This is a clear, usable introduction to data mining: the data it uses, the questions it answers, and the techniques for connecting them. It gives codable detail for lots of techniques, and prepares the reader for more advanced discussions. I recommend it very highly.

//wiredweird
27 von 30 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Just right 15. November 2000
Von Matthew M. Shannon - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe|Verifizierter Kauf
I've been working with Data Warehousing for a few years, and stumbled upon this book here on Amazon a few weeks ago. I was leery at first because of it's obvious textbook price/look, but purchased it anyway, much to my delight.
The book provides a very vendor neutral view of Data Warehousing and Data Mining, many data mining ideas and examples are presented throughout the book without any specific programming language used. I feel it allows you to implement the idea in your preferred method.
I found the book more than worth the price, in fact I was asked to give a guest lecture/presentation at a University Data Mining class in the Spring and will definitely pull from this book for my presentation.
Enjoy!
28 von 32 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Poorly worded. No depth 3. März 2003
Von Ein Kunde - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
Dismal notation coupled with incomplete or incoherent explanations make this book frustrating to read.
The author needlessly inserts layers of abstraction, making otherwise simple concepts and formulas unnecessarily time-consuming to understand.
The provided examples do make up for some of the deficiencies of the author's notation and poor wording, but not enough to make this book worth buying.
The book covers many topics but does not go into sufficient depth. It's too technical for managers and not rigorous enough for technical professionals wishing to use data mining to solve real problems. If you are new to data mining, you may learn some useful overall concepts, but won't learn enough to apply them effectively. Experts should definitely look somewhere else.
22 von 25 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Deep, comprehensive and practical + data mining software 25. September 2001
Von Ein Kunde - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
I learned about Data Mining - Concepts and Techniques from a friend who is a CS professor. He is using this book to teach his graduate and undergraduate classes and he said that the same book is also used by many leading universities such as Cornell, UC Berkeley, Georgia Tech.
First I thought this book would be hard for me to follow because I do not have a degree in CS, and I just wanted to have a good comprehensive understanding of most technical data mining methods so I can advise my IT clients (I have read several other general data mining books, and they are not technical enough for me). I was pleasantly surprised by both the depth & scope of this book and its readability. Granted it requires more brain power than some other general books covering data mining and CRMs, but after reading this book, I feel I can talk and act like an expert.
The book also has a forward written by Jim Gray. Jim Gray received the A.M. Turing award, widely regarded in industry circles as the Nobel Prize of computer science. In his early career Jim Gray worked with Ted Codd, the father of "relational databases," the modern database model in use today for more
Jim Gray said he learned a lot from this data mining book...
There is also a companion software called DBMiner for this book, and one can get hand-on experience for data mining techniques such as association, classification, OLAP visualizer and clustering. I downloaded the software from the web site...
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