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دانلود کتاب Data Mining
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مشخصات کتاب
Data Mining
ویرایش: 3rd ed
نویسندگان: Jiawei Han. Micheline Kamber and Jian Pei (Auth.)
سری:
ISBN (شابک) : 9780123814791
ناشر: Elsevier
سال نشر: 2012
تعداد صفحات: 718
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 13 مگابایت
قیمت کتاب (تومان) : 52,000
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توضیحاتی درمورد کتاب به خارجی
-
"[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."--ACMs Computing Reviews.comWe 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,
KDnuggetsJiawei,
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. Its 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 texts
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 books 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
Front Matter, Pages i-v
Copyright, Page vi
Dedication, Page vii
Foreword, Pages xix-xx
Foreword to Second Edition, Pages xxi-xxii
Preface, Pages xxiii-xxix
Acknowledgments, Pages xxxi-xxxiii
About the Authors, Page xxxv
1 - Introduction, Pages 1-38
2 - Getting to Know Your Data, Pages 39-82
3 - Data Preprocessing, Pages 83-124
4 - Data Warehousing and Online Analytical Processing, Pages 125-185
5 - Data Cube Technology, Pages 187-242
6 - Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods, Pages 243-278
7 - Advanced Pattern Mining, Pages 279-325
8 - Classification: Basic Concepts, Pages 327-391
9 - Classification: Advanced Methods, Pages 393-442
10 - Cluster Analysis: Basic Concepts and Methods, Pages 443-495
11 - Advanced Cluster Analysis, Pages 497-541
12 - Outlier Detection, Pages 543-584
13 - Data Mining Trends and Research Frontiers, Pages 585-631
Bibliography, Pages 633-671
Index, Pages 673-703
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