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 most recent strategies and advancements in information mining, hence making this a entire source for either newbies and practitioners. the point of interest is data-all facets. The presentation is huge, encyclopedic, and entire, with plentiful references for readers to pursue in-depth examine on any procedure. Summing Up: hugely steered. Upper-division undergraduates via professionals/practitioners."--CHOICE"This attention-grabbing and accomplished advent to information mining emphasizes the curiosity in multidimensional facts mining--the integration of on-line analytical processing (OLAP) and knowledge mining. a few chapters conceal simple equipment, and others specialize in complex options. The constitution, besides the didactic presentation, makes the e-book appropriate for either novices and really good readers."--
ACMs Computing Reviews.comWe live within the info deluge age. The Data Mining: options and Techniquesexhibits us how to define worthy wisdom in all that facts. Thise third editionThird variation considerably expands the center chapters on information preprocessing, widespread trend mining, type, and clustering. The bookIt additionally comprehensively covers OLAP and outlier detection, and examines mining networks, complicated info varieties, and significant program components. The publication, with its significant other site, may make an exceptional textbook for analytics, facts mining, and information discovery courses.-- Gregory Piatetsky, President, KDnuggetsJiawei, Micheline, and Jian supply an encyclopaedic insurance of the entire similar equipment, from the vintage subject matters of clustering and type, to database equipment (association principles, information cubes) to newer and complicated themes (SVD/PCA , wavelets, help vector machines) . total, it really is a superb ebook on vintage and glossy information mining tools alike, and it's excellent not just for educating, yet as a reference book.- From the foreword via Christos Faloutsos, Carnegie Mellon University "A excellent textbook on info mining, this 3rd version displays the alterations which are taking place within the info mining box. It provides mentioned fabric from approximately 2006, a brand new part on visualization, and trend mining with the newer cluster tools. Its a well-written textual content, with the entire assisting fabrics an teacher is probably going to need, together with internet fabric help, broad challenge units, and answer manuals. although it serves as a knowledge mining textual content, readers with little event within the zone will locate it readable and enlightening. That being acknowledged, readers are anticipated to have a few coding adventure, in addition to database layout and facts research wisdom extra goods are important of notice: the texts bibliography is a superb reference checklist for mining learn; and the index is particularly whole, which makes it effortless to find info. 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 machine technology, Simon Fraser U., British Columbia) current a textbook for a complicated undergraduate or starting graduate direction introducing information mining. scholars must have a few history in facts, database structures, and desktop studying and a few adventure programming. one of the subject matters have become to understand the information, facts warehousing and on-line analytical processing, info dice know-how, cluster research, detecting outliers, and developments and learn frontiers. Chapter-end workouts are included."--SciTech booklet News "This booklet is an in depth and distinct consultant to the valuable rules, innovations and applied sciences of knowledge mining. The ebook is organised in thirteen sizeable chapters, every one of that is basically standalone, yet with valuable references to the books insurance of underlying options. A huge diversity of themes are lined, from an preliminary evaluation of the sector of information mining and its basic techniques, to facts instruction, information warehousing, OLAP, trend discovery and information category. the ultimate bankruptcy describes the present kingdom of information mining study and energetic learn 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 - learning Your Data
, Pages 39-82
3 - information Preprocessing
, Pages 83-124
4 - information Warehousing and on-line Analytical Processing
, Pages 125-185
5 - info dice Technology
, Pages 187-242
6 - Mining widespread styles, institutions, and Correlations: simple thoughts and Methods
, Pages 243-278
7 - complex development Mining
, Pages 279-325
8 - category: easy Concepts
, Pages 327-391
9 - category: complicated Methods
, Pages 393-442
10 - Cluster research: uncomplicated strategies and Methods
, Pages 443-495
11 - complicated Cluster Analysis
, Pages 497-541
12 - Outlier Detection
, Pages 543-584
13 - information Mining developments and examine Frontiers
, Pages 585-631
, Pages 633-671
, Pages 673-703
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Additional resources for Data Mining
4 What Kinds of Patterns Can Be Mined? 17 Concept description, including characterization and discrimination, is described in Chapter 4. 2 Mining Frequent Patterns, Associations, and Correlations Frequent patterns, as the name suggests, are patterns that occur frequently in data. There are many kinds of frequent patterns, including frequent itemsets, frequent subsequences (also known as sequential patterns), and frequent substructures. A frequent itemset typically refers to a set of items that often appear together in a transactional data set—for example, milk and bread, which are frequently bought together in grocery stores by many customers.
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. Interesting areas of research include how to interact with a data mining system, how to incorporate a user’s background knowledge in mining, and how to visualize and comprehend data mining results. We introduce each of these here. Interactive mining: The data mining process should be highly interactive.
An interesting pattern represents knowledge. Several objective measures of pattern interestingness exist. These are based on the structure of discovered patterns and the statistics underlying them. An objective measure for association rules of the form X ⇒ Y is rule support, representing the percentage of transactions from a transaction database that the given rule satisfies. This is taken to be the probability P(X ∪ Y ), where X ∪ Y indicates that a transaction contains both X and Y , that is, the union of itemsets X and Y .