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Pressestimmen
"A well-written textbook (2nd ed., 2006; 1st ed., 2001) on data mining or knowledge discovery. The text is supported by a strong outline. The authors preserve much of the introductory material, but add the latest techniques and developments in data mining, thus making this a comprehensive resource for both beginners and practitioners. The focus is data―all aspects. The presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique. Summing Up: Highly recommended. Upper-division undergraduates through professionals/practitioners." --CHOICE "This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining--the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers." --ACM’s Computing Reviews.com "We are living in the data deluge age. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. Thise 3rd editionThird Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The bookIt also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. The book, with its companion website, would make a great textbook for analytics, data mining, and knowledge discovery courses." --Gregory Piatetsky, President, KDnuggets "Jiawei, Micheline, and Jian give an encyclopaedic coverage of all the related methods, from the classic topics of clustering and classification, to database methods (association rules, data cubes) to more recent and advanced topics (SVD/PCA , wavelets, support vector machines)…. Overall, it is an excellent book on classic and modern data mining methods alike, and it is ideal not only for teaching, but as a reference book." --From the foreword by Christos Faloutsos, Carnegie Mellon University "A very good textbook on data mining, this third edition reflects the changes that are occurring in the data mining field. It adds cited material from about 2006, a new section on visualization, and pattern mining with the more recent cluster methods. It’s a well-written text, with all of the supporting materials an instructor is likely to want, including Web material support, extensive problem sets, and solution manuals. Though it serves as a data mining text, readers with little experience in the area will find it readable and enlightening. That being said, readers are expected to have some coding experience, as well as database design and statistics analysis knowledge…Two additional items are worthy of note: the text’s bibliography is an excellent reference list for mining research; and the index is very complete, which makes it easy to locate information. Also, researchers and analysts from other disciplines--for example, epidemiologists, financial analysts, and psychometric researchers--may find the material very useful." --Computing Reviews "Han (engineering, U. of Illinois-Urbana-Champaign), Micheline Kamber, and Jian Pei (both computer science, Simon Fraser U., British Columbia) present a textbook for an advanced undergraduate or beginning graduate course introducing data mining. Students should have some background in statistics, database systems, and machine learning and some experience programming. Among the topics are getting to know the data, data warehousing and online analytical processing, data cube technology, cluster analysis, detecting outliers, and trends and research frontiers. Chapter-end exercises are included." --SciTech Book News "This book is an extensive and detailed guide to the principal ideas, techniques and technologies of data mining. The book is organised in 13 substantial chapters, each of which is essentially standalone, but with useful references to the book’s coverage of underlying concepts. A broad range of topics are covered, from an initial overview of the field of data mining and its fundamental concepts, to data preparation, data warehousing, OLAP, pattern discovery and data classification. The final chapter describes the current state of data mining research and active research areas." --BCS.org
Rezension
A comprehensive and practical look at the concepts and techniques you need in the area of data mining and knowledge discovery
Alle Produktbeschreibungen
Produktinformation
Leseprobe Jetzt reinlesen [602kb PDF]
Gebundene Ausgabe: 744 Seiten
Verlag: Morgan Kaufmann; Auflage: 3rd revised edition (25. Juli 2011)
Sprache: Englisch
ISBN-10: 9780123814791
ISBN-13: 978-9380931913
ASIN: 0123814790
Größe und/oder Gewicht:
20,3 x 4,4 x 24,1 cm
Durchschnittliche Kundenbewertung:
4.4 von 5 Sternen
3 Kundenrezensionen
Amazon Bestseller-Rang:
Nr. 85.705 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Das Buch führt den Leser sehr behutsam in die Materie ein. Nichts für Leser, die konzentrierten Inhalt wünschen und nicht 700 Seiten hauchdünn ausgewallter Inhalt lesen möchten. Das Buch ist aber sehr gut für solche geeignet, die anhand von verschiedenen Beispielen sich in dieses Thema einarbeiten wollen. Da kommt jeder mit! Das Buch ist sehr gut strukturiert und an jedem Kapitelanfang wird erklärt, was im Folgenden auf den Leser wartet. Detailliertere mathematische Grundlagen werden nicht behandelt, aber man gewinnt einen guten Einblick in die Möglichkeiten und Prinzipien des Data Mining
Dieses sehr umfangreiche Werk kann man getrost als Standardwerk für Data Mining-Techniken bezeichnen. Es geht davon aus, dass man große Datenbestände und auch eine Datenbank bzw. sogar ein Data Warehouse besitzt, aus dem die Daten abgerufen werden. Bei der Darstellung der verschiedenen Konzepte und Algorithmen folgen immer auch Hinweise darauf, wie sich die beschriebenen Algorithmen bei sehr großen Datenbeständen verhalten. Ansonsten handelt es sich um ein Buch für Studenten, das von den Autoren in Vorlesungen eingesetzt wird. Dadurch ist es sehr breit angelegt, geht evtl. nicht sehr tief, hat aber derart viele Themen, dass man dennoch sehr lange mit dem Buch "auskommt", wenn man Data Mining einsetzen möchte, ehe man zu weiteren Werken greifen müsste. Nach einem Einführungsteil, der auch OLAP und Data Warehousing streift, folgt eine umfangreiche Darstellung zu verschiedenen Data Mining-Algorithmen. Dieser Hauptteil des Buchs enthält viele Beispiele, ausführliche Darstellungen und begrenzt mathematische und statistische Konzepte. Das letzte Drittel enthält eine Darstellung von sehr fortgeschrittenen Data Mining-Techniken, die man vermutlich in der Praxis bei Controlling, Marketing etc. nicht verwenden kann, die aber das Themengebiet an sich hervorragend abrunden und für eine "spätere Lektüre", wenn man alles Andere nachvollzogen hat, verwenden kann.
Data Mining: Concepts and Techniques book is the best book for people who want to get an idea about the field of data mining. It gives a good comprehensive over view for the major topics discussed in the field.
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