By Jiawei Han, Micheline Kamber and Jian Pei (Auth.)
"[A] well-written textbook (2nd ed., 2006; 1st ed., 2001) on information mining or wisdom discovery. The textual content is supported through a powerful define. The authors safeguard a lot of the introductory fabric, yet upload the most recent thoughts and advancements in information mining, hence making this a finished source for either newbies and practitioners. the focal point is data-all elements. The presentation is wide, encyclopedic, and accomplished, with considerable references for readers to pursue in-depth learn on any procedure. Summing Up: hugely steered. Upper-division undergraduates via professionals/practitioners."--CHOICE"This fascinating and complete advent to facts mining emphasizes the curiosity in multidimensional info mining--the integration of on-line analytical processing (OLAP) and information mining. a few chapters conceal simple tools, and others specialize in complicated options. The constitution, in addition to the didactic presentation, makes the booklet compatible for either rookies and really good readers."--
ACMs Computing Reviews.comWe live within the information deluge age. The Data Mining: techniques and Techniquesindicates us how to define helpful wisdom in all that info. Thise third editionThird version considerably expands the middle chapters on facts preprocessing, common trend mining, category, and clustering. The bookIt additionally comprehensively covers OLAP and outlier detection, and examines mining networks, advanced information kinds, and critical software parts. The ebook, with its spouse site, may make a superb textbook for analytics, information mining, and information discovery courses.-- Gregory Piatetsky, President, KDnuggetsJiawei, Micheline, and Jian supply an encyclopaedic assurance of all of the similar equipment, from the vintage themes of clustering and class, to database equipment (association ideas, information cubes) to newer and complicated subject matters (SVD/PCA , wavelets, help vector machines) . total, it really is a very good ebook on vintage and smooth facts mining tools alike, and it really is excellent not just for instructing, yet as a reference book.- From the foreword via Christos Faloutsos, Carnegie Mellon University "A excellent textbook on facts mining, this 3rd version 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 more moderen cluster tools. Its a well-written textual content, with the entire assisting fabrics an teacher is probably going to wish, together with internet fabric help, wide challenge units, and resolution manuals. even though it serves as a knowledge mining textual content, readers with little adventure within the quarter will locate it readable and enlightening. That being stated, readers are anticipated to have a few coding adventure, in addition to database layout and information research wisdom extra goods are useful of observe: the texts bibliography is a wonderful reference checklist for mining learn; and the index is especially entire, 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 path introducing information mining. scholars must have a few history in data, database platforms, and desktop studying and a few event programming. one of the themes have become to grasp the information, info warehousing and on-line analytical processing, facts dice expertise, cluster research, detecting outliers, and tendencies and examine frontiers. Chapter-end workouts are included."--SciTech ebook News "This ebook is an in depth and distinct consultant to the primary principles, strategies and applied sciences of knowledge mining. The ebook is organised in thirteen great chapters, each one of that is primarily standalone, yet with important references to the books insurance of underlying techniques. A extensive variety of issues are coated, from an preliminary assessment of the sector of knowledge mining and its basic suggestions, to facts training, information warehousing, OLAP, trend discovery and knowledge category. the ultimate bankruptcy describes the present nation of information mining study and lively study areas." --BCS.org
, 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 - studying Your Data
, Pages 39-82
3 - info Preprocessing
, Pages 83-124
4 - facts Warehousing and on-line Analytical Processing
, Pages 125-185
5 - facts dice Technology
, Pages 187-242
6 - Mining common styles, institutions, and Correlations: easy techniques and Methods
, Pages 243-278
7 - complicated development Mining
, Pages 279-325
8 - class: uncomplicated Concepts
, Pages 327-391
9 - type: complicated Methods
, Pages 393-442
10 - Cluster research: uncomplicated ideas and Methods
, Pages 443-495
11 - complex Cluster Analysis
, Pages 497-541
12 - Outlier Detection
, Pages 543-584
13 - information Mining traits and study Frontiers
, Pages 585-631
, Pages 633-671
, Pages 673-703
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Extra resources for Data Mining
The data are stored to provide information from a historical perspective, such as in the past 6 to 12 months, and are typically summarized. For example, rather than storing the details of each sales transaction, the data warehouse may store a summary of the transactions per item type for each store or, summarized to a higher level, for each sales region. 3 What Kinds of Data Can Be Mined? 6 Typical framework of a data warehouse for AllElectronics. or sum(sales amount). A data cube provides a multidimensional view of data and allows the precomputation and fast access of summarized data.
Middle_aged, senior youth income? 9 A classification model can be represented in various forms: (a) IF-THEN rules, (b) a decision tree, or (c) a neural network. 4 What Kinds of Patterns Can Be Mined? 19 be converted to classification rules. A neural network, when used for classification, is typically a collection of neuron-like processing units with weighted connections between the units. There are many other methods for constructing classification models, such as na¨ıve Bayesian classification, support vector machines, and k-nearest-neighbor classification.
Let us now examine the kinds of patterns that can be mined. There are a number of data mining functionalities. 5). Data mining functionalities are used to specify the kinds of patterns to be found in data mining tasks. In general, such tasks can be classified into two categories: descriptive and predictive. Descriptive mining tasks characterize properties of the data in a target data set. Predictive mining tasks perform induction on the current data in order to make predictions. Data mining functionalities, and the kinds of patterns they can discover, are described below.