New PDF release: Data Mining

By Jiawei Han, Micheline Kamber and Jian Pei (Auth.)

ISBN-10: 0123814790

ISBN-13: 9780123814791

  • "[A] well-written textbook (2nd ed., 2006; 1st ed., 2001) on information mining or wisdom discovery. The textual content is supported via a robust define. The authors shield a lot of the introductory fabric, yet upload the newest innovations and advancements in information mining, hence making this a finished source for either newcomers and practitioners. the point of interest is data-all features. The presentation is extensive, encyclopedic, and finished, with plentiful references for readers to pursue in-depth learn on any method. Summing Up: hugely suggested. Upper-division undergraduates via professionals/practitioners."--CHOICE

    "This fascinating and accomplished creation to information mining emphasizes the curiosity in multidimensional information mining--the integration of on-line analytical processing (OLAP) and information mining. a few chapters hide simple tools, and others concentrate on complicated strategies. The constitution, besides the didactic presentation, makes the ebook compatible for either newcomers and really expert readers."--ACM’s Computing

    We reside within the information deluge age. The Data Mining: recommendations and Techniques indicates us how to define important wisdom in all that info. Thise third editionThird version considerably expands the center chapters on information preprocessing, common trend mining, type, and clustering. The bookIt additionally comprehensively covers OLAP and outlier detection, and examines mining networks, complicated info kinds, and critical program parts. The ebook, with its significant other site, might make an excellent textbook for analytics, info mining, and data discovery courses.--Gregory Piatetsky, President, KDnuggets

    Jiawei, Micheline, and Jian provide an encyclopaedic insurance of all of the comparable equipment, from the vintage themes of clustering and class, to database tools (association ideas, information cubes) to more moderen and complicated issues (SVD/PCA , wavelets, help vector machines) . total, it really is a very good booklet on vintage and sleek facts mining tools alike, and it truly is perfect not just for educating, yet as a reference book.-From the foreword through Christos Faloutsos, Carnegie Mellon University

    "A excellent textbook on info mining, this 3rd variation displays the alterations which are taking place within the facts mining box. It provides mentioned fabric from approximately 2006, a brand new part on visualization, and trend mining with the newer cluster tools. It’s a well-written textual content, with all the helping fabrics an teacher is probably going to wish, together with net fabric aid, huge challenge units, and answer manuals. even though it serves as a knowledge mining textual content, readers with little event within the zone will locate it readable and enlightening. That being stated, readers are anticipated to have a few coding event, in addition to database layout and facts research wisdom extra goods are worthwhile of notice: the text’s bibliography is a wonderful reference record for mining learn; and the index is particularly whole, which makes it effortless to find details. additionally, researchers and analysts from different disciplines--for instance, epidemiologists, monetary analysts, and psychometric researchers--may locate the cloth very useful."--Computing Reviews

    "Han (engineering, U. of Illinois-Urbana-Champaign), Micheline Kamber, and Jian Pei (both laptop technological know-how, Simon Fraser U., British Columbia) current a textbook for a complicated undergraduate or starting graduate direction introducing information mining. scholars must have a few historical past in facts, database structures, and computer studying and a few event programming. one of the issues are becoming to understand the knowledge, info warehousing and on-line analytical processing, info dice expertise, cluster research, detecting outliers, and traits and examine frontiers. Chapter-end routines are included."--SciTech ebook News

    "This publication is an intensive and precise advisor to the vital rules, ideas and applied sciences of knowledge mining. The e-book is organised in thirteen massive chapters, every one of that is basically standalone, yet with necessary references to the book’s assurance of underlying innovations. A large diversity of issues are coated, from an preliminary review of the sphere of knowledge mining and its basic options, to facts instruction, facts warehousing, OLAP, development discovery and knowledge type. the ultimate bankruptcy describes the present kingdom of knowledge mining examine and energetic study areas."

Front Matter

, Pages i-v

, Page vi

, Page vii

, Pages xix-xx
Foreword to moment Edition

, Pages xxi-xxii

, Pages xxiii-xxix

, Pages xxxi-xxxiii
About the Authors

, Page xxxv
1 - Introduction

, Pages 1-38
2 - discovering Your Data

, Pages 39-82
3 - facts Preprocessing

, Pages 83-124
4 - facts Warehousing and on-line Analytical Processing

, Pages 125-185
5 - information dice Technology

, Pages 187-242
6 - Mining widespread styles, institutions, and Correlations: simple thoughts and Methods

, Pages 243-278
7 - complex trend Mining

, Pages 279-325
8 - class: easy Concepts

, Pages 327-391
9 - category: complicated Methods

, Pages 393-442
10 - Cluster research: easy innovations and Methods

, Pages 443-495
11 - complex Cluster Analysis

, Pages 497-541
12 - Outlier Detection

, Pages 543-584
13 - facts Mining developments and study Frontiers

, Pages 585-631

, Pages 633-671

, Pages 673-703

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Extra resources for Data Mining

Sample text

For classification and clustering tasks, machine learning research often focuses on the accuracy of the model. In addition to accuracy, data mining research places strong emphasis on the efficiency and scalability of mining methods on large data sets, as well as on ways to handle complex types of data and explore new, alternative methods. 3 Database Systems and Data Warehouses Database systems research focuses on the creation, maintenance, and use of databases for organizations and end-users. Particularly, database systems researchers have established highly recognized principles in data models, query languages, query processing and optimization methods, data storage, and indexing and accessing methods.

The data mining step may interact with the user or a knowledge base. The interesting patterns are presented to the user and may be stored as new knowledge in the knowledge base. The preceding view shows data mining as one step in the knowledge discovery process, albeit an essential one because it uncovers hidden patterns for evaluation. However, in industry, in media, and in the research milieu, the term data mining is often used to refer to the entire knowledge discovery process (perhaps because the term is shorter than knowledge discovery from data).

3 Transactional Data In general, each record in a transactional database captures a transaction, such as a customer’s purchase, a flight booking, or a user’s clicks on a web page. A transaction typically includes a unique transaction identity number (trans ID) and a list of the items making up the transaction, such as the items purchased in the transaction. A transactional database may have additional tables, which contain other information related to the transactions, such as item description, information about the salesperson or the branch, and so on.

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Data Mining by Jiawei Han, Micheline Kamber and Jian Pei (Auth.)

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