DATA MINING JIAWEI HAN AND MICHELINE KAMBER EBOOK
Data Mining: Concepts and Techniques, 3rd Edition. Jiawei Han, Micheline Kamber, Jian Pei. Database Modeling and Design: Logical Design, 5th Edition. Authors: Jiawei Han Micheline Kamber Jian Pei. Hardcover ISBN: eBook ISBN: Imprint: Morgan Kaufmann. Published Date. Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) [Jiawei Han, Micheline Kamber, Jian Pei] on.
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by Jiawei Han, Jian Pei, Micheline Kamber Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or. Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques,. The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor Morgan Kaufmann Data Warehouse and OLAP Technology for Data Mining. Data Mining: Concepts and Techniques equips you with a sound Provides in- depth, practical coverage of essential data mining topics, including Rent and save from the world's largest eBookstore. Jiawei Han Editor, Micheline Kamber.
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.
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It adds cited material from about , a new section on visualization, and pattern mining with the more recent cluster methods. Though it serves as a data mining text, readers with little experience in the area will find it readable and enlightening. 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.
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Table of Contents
You're using an out-of-date version of Internet Explorer. Log In Sign Up. Data mining: Data Mining: A present- with huge databases which have to be day gold rush. Data Mining is a automatically analyzed. Its name stems from the transactional, object-oriented, spatial, idea of mining knowledge from large temporal, text, and legacy databases, as well amounts of data.
The tools it provides assist as data warehouses and the World Wide us in the discovery of relevant information Web. The patterns obtained are used to through a wide range of data analysis describe concepts, to analyze associations, to techniques.
Any method used to extract build classification and regression models, to patterns from a given data source is cluster data, to model trends in time-series, considered to be a data mining technique.
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
Since the patterns which interested in this eclectic research field. The are present in data are not all equally useful, book surveys techniques for the main tasks interestingness measures are needed to data miners have to perform.
Most existing estimate the relevance of the discovered data mining texts emphasize the managerial patterns to guide the mining process. The warehousing and multidimensional databases evolution of database technology is an are introduced as desirable intermediate essential prerequisite for understanding the layers between the original data sources and need of knowledge discovery in databases the On-Line Analytical Mining system the KDD.
This evolution is described in the user interacts with. OLAM also known as book to present data mining as a natural stage OLAP mining integrates on-line analytical in the data processing history: 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.
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Several classification final goal, data mining techniques can be and regression techniques are introduced considered to be descriptive or predictive: 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.Among the topics are getting to know the data, data warehousing and online analytical processing, data cube technology, cluster analysis, detecting outliers, and trends and research frontiers.
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