Data mining book by kamber

 

    Data mining: concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei. .. The book gives quick introductions to database and data mining concepts. Although advances in data mining technology have made extensive data collection much easier. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers. .. Micheline Kamber. The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor Morgan Errata on the first and second printings of the book · Errata on.

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    Data Mining Book By Kamber

    download Data Mining: Concepts and Techniques - 3rd Edition. Print Book & E- Book. Authors: Jiawei Han Micheline Kamber Jian Pei. Hardcover ISBN. patterns from a given data source is. considered to be a data mining technique. Han and Kamber's book provides. more than a good starting point for those. and Micheline Kamber, c c Morgan Kaufmann Publishers. This book explores the concepts and techniques of data mining, a promising and flourishing .

    Or, get it for Kobo Super Points! See if you have enough points for this item. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing OLAP , and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data download the eBook.

    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. 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.

    Although advances in data mining technology have made extensive data collection much easier, it's still evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining.

    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 presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique. Summing Up: Highly recommended.

    Data Mining by Micheline Kamber

    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.

    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. 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.

    Chapter-end exercises are included. A broad range of topics are covered, from an initial overview of the field of data mining and its fundamental concepts, to data preparation, data warehousing, OLAP, pattern discovery and data classification. The final chapter describes the current state of data mining research and active research areas. Would you like to tell us about a lower price? If you are a seller for this product, would you like to suggest updates through seller support?

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    Frequently bought together. Total price: Add all three to Cart Add all three to List. One of these items ships sooner than the other. Show details. download the selected items together This item: Data Mining: Ships from and sold by site. FREE Shipping.

    Data Science for Business: Customers who bought this item also bought. Page 1 of 1 Start over Page 1 of 1. Ian H. 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.

    Data Mining: Micheline Kamber: rutalchondbulsio.cf: Books

    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.

    Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

    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.

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