Data mining: concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei. .. The book gives quick introductions to database and data mining concepts. Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) [Jiawei Han, Micheline Kamber, Jian Pei] on. Compre o livro Data Mining: Concepts and Techniques na usaascvb.info: confira as ofertas para livros em inglês e importados.
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download Data Mining: Concepts and Techniques - 3rd Edition. Print Book & E- Book. ISBN , Data Mining: Concepts and Techniques, Errata on the first and second printings of the book Data Warehouse and OLAP Technology for Data Mining. Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which.
Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data.
Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data.
This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. 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. 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.
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. 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.
It adds cited material from about , a new section on visualization, and pattern mining with the more recent cluster methods. It's 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 text's 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. Students should have some background in statistics, database systems, and machine learning and some experience programming.
OLAM also known as book to present data mining as a natural stage OLAP mining integrates on-line analytical in the data processing history: we have processing with data mining.
Several improvements over the Mining is an alternative to this language and original Apriori algorithm are also described.
Han et al. Additional before applying data mining algorithms.
Data extensions to the basic association rule cleaning, data integration, data framework are explored, e. All these techniques are artificially categorized into quantitative and explained in the book without focusing too distance-based association rules when both of much on implementation details so that the them work with quantitative attributes.
According to their unsupervised learning.
Several classification final goal, data mining techniques can be and regression techniques are introduced considered to be descriptive or predictive: taking into account accuracy, speed, Descriptive data mining intends to summarize robustness, scalability, and interpretability data and to highlight their interesting issues.
The authors also discuss some summarize data by applying attribute- classification methods based on concepts oriented induction using characteristic rules from association rule mining.
Furthermore, and generalized relations. Analytical the chapter on classification mentions characterization is used to perform attribute alternative models based on instance-based relevance measurements to identify irrelevant learning e.
We believe number of attributes, the more efficient the that this book section would deserve a more mining process. Generalization techniques detailed treatment even a whole volume on can also be extended to discriminate among its own , which should obviously include an different classes. The discussion of descriptive techniques is Regression called prediction by the completed with a brief study of statistical authors appears as an extension of the measures i.
The former dispersion measures and their insightful deals with continuous values while the latter graphical display.
Association rules are midway Linear regression is clearly explained; between descriptive and predictive data multiple, nonlinear, generalized linear, and mining maybe closer to descriptive log-linear regression models are only techniques. They find interesting referenced in the text. Some ratio-scaled.
A taxonomy of clustering buzzwordism about the role of data mining methods is proposed including examples for and its social impact can be found in this each category: partitioning methods e.
This categorization of clustering Why to Read This Book.