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

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

    "This attention-grabbing and entire advent to info mining emphasizes the curiosity in multidimensional information mining--the integration of on-line analytical processing (OLAP) and information mining. a few chapters disguise uncomplicated equipment, and others concentrate on complex options. The constitution, besides the didactic presentation, makes the ebook appropriate for either novices and really expert readers."--ACM’s Computing Reviews.com

    We live within the facts deluge age. The Data Mining: ideas and Techniques exhibits us how to define helpful wisdom in all that facts. Thise third editionThird variation considerably expands the center chapters on facts preprocessing, widespread development mining, class, and clustering. The bookIt additionally comprehensively covers OLAP and outlier detection, and examines mining networks, complicated facts kinds, and critical software components. The e-book, with its better half site, could make an excellent textbook for analytics, info mining, and data discovery courses.--Gregory Piatetsky, President, KDnuggets

    Jiawei, Micheline, and Jian supply an encyclopaedic insurance of all of the comparable tools, from the vintage themes of clustering and class, to database tools (association principles, information cubes) to newer and complicated subject matters (SVD/PCA , wavelets, aid vector machines) . total, it truly is an outstanding publication on vintage and glossy information mining equipment alike, and it truly is excellent not just for educating, yet as a reference book.-From the foreword by means of Christos Faloutsos, Carnegie Mellon University

    "A excellent textbook on info mining, this 3rd variation displays the alterations which are taking place within the information mining box. It provides mentioned fabric from approximately 2006, a brand new part on visualization, and trend mining with the newer cluster equipment. It’s a well-written textual content, with all the aiding fabrics an teacher is probably going to wish, together with net fabric help, wide challenge units, and answer manuals. even though it serves as an information mining textual content, readers with little event within the sector will locate it readable and enlightening. That being stated, readers are anticipated to have a few coding adventure, in addition to database layout and data research wisdom extra goods are helpful of word: the text’s bibliography is a superb reference record for mining learn; and the index is especially 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 fabric very useful."--Computing Reviews

    "Han (engineering, U. of Illinois-Urbana-Champaign), Micheline Kamber, and Jian Pei (both desktop technology, Simon Fraser U., British Columbia) current a textbook for a sophisticated undergraduate or starting graduate direction introducing information mining. scholars must have a few heritage in facts, database platforms, and computer studying and a few event programming. one of the issues are becoming to grasp the knowledge, information warehousing and on-line analytical processing, information dice expertise, cluster research, detecting outliers, and developments and learn frontiers. Chapter-end routines are included."--SciTech e-book News


    "This booklet is an in depth and targeted advisor to the vital rules, options and applied sciences of information mining. The booklet is organised in thirteen large chapters, each one of that is basically standalone, yet with worthwhile references to the book’s insurance of underlying innovations. A extensive variety of themes are coated, from an preliminary assessment of the sector of knowledge mining and its basic ideas, to facts training, facts warehousing, OLAP, trend discovery and information category. the ultimate bankruptcy describes the present country of information mining study and lively learn areas."--BCS.org


Content:
Front Matter

, Pages i-v
Copyright

, Page vi
Dedication

, Page vii
Foreword

, Pages xix-xx
Foreword to moment Edition

, Pages xxi-xxii
Preface

, Pages xxiii-xxix
Acknowledgments

, Pages xxxi-xxxiii
About the Authors

, Page xxxv
1 - Introduction

, Pages 1-38
2 - researching Your Data

, Pages 39-82
3 - info Preprocessing

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

, Pages 125-185
5 - information dice Technology

, Pages 187-242
6 - Mining common styles, institutions, and Correlations: easy innovations and Methods

, Pages 243-278
7 - complicated trend Mining

, Pages 279-325
8 - class: simple Concepts

, Pages 327-391
9 - type: complicated Methods

, Pages 393-442
10 - Cluster research: simple suggestions and Methods

, Pages 443-495
11 - complex Cluster Analysis

, Pages 497-541
12 - Outlier Detection

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

, Pages 585-631
Bibliography

, Pages 633-671
Index

, Pages 673-703

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Extra info for Data Mining

Sample text

Data cleaning, data preprocessing, outlier detection and removal, and uncertainty reasoning are examples of techniques that need to be integrated with the data mining process. Pattern evaluation and pattern- or constraint-guided mining: Not all the patterns generated by data mining processes are interesting. What makes a pattern interesting may vary from user to user. Therefore, techniques are needed to assess the interestingness of discovered patterns based on subjective measures. These estimate the value of patterns with respect to a given user class, based on user beliefs or expectations.

What makes a pattern interesting may vary from user to user. Therefore, techniques are needed to assess the interestingness of discovered patterns based on subjective measures. These estimate the value of patterns with respect to a given user class, based on user beliefs or expectations. Moreover, by using interestingness measures or user-specified constraints to guide the discovery process, we may generate more interesting patterns and reduce the search space. 2 User Interaction The user plays an important role in the data mining process.

Alternatively, data mining tasks can be built on top of statistical models. For example, we can use statistics to model noise and missing data values. Then, when mining patterns in a large data set, the data mining process can use the model to help identify and handle noisy or missing values in the data. Statistics research develops tools for prediction and forecasting using data and statistical models. Statistical methods can be used to summarize or describe a collection of data. Basic statistical descriptions of data are introduced in Chapter 2.

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