Data Mining Southeast Asia Edition

Author : Jiawei Han,Jian Pei,Micheline Kamber
Publisher : Elsevier
Page : 800 pages
File Size : 40,8 Mb
Release : 2006-04-06
Category : Computers
ISBN : 0080475582

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Data Mining, Southeast Asia Edition by Jiawei Han,Jian Pei,Micheline Kamber Pdf

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects Complete classroom support for instructors at www.mkp.com/datamining2e companion site

Author : Jiawei Han
Publisher : Morgan Kaufmann
Page : 770 pages
File Size : 55,9 Mb
Release : 2006
Category : Computers
ISBN : 1558609016

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Data Mining by Jiawei Han Pdf

Expanding and updating the premier professional reference on data mining concepts and techniques, the second edition of this comprehensive and state-of-the-art text combines sound theory with truly practical applications to prepare database practitioners and professionals for real-world challenges in the professional database field. Includes approximately 100 pages of new material.

Author : Jiawei Han,Jian Pei,Hanghang Tong
Publisher : Morgan Kaufmann
Page : 786 pages
File Size : 55,5 Mb
Release : 2022-07-02
Category : Computers
ISBN : 9780128117613

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Data Mining by Jiawei Han,Jian Pei,Hanghang Tong Pdf

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining. Presents a comprehensive new chapter on deep learning, including improving training of deep learning models, convolutional neural networks, recurrent neural networks, and graph neural networks Addresses advanced topics in one dedicated chapter: data mining trends and research frontiers, including mining rich data types (text, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagattion), data mining methodologie and systems, and data mining and society Provides a comprehensive, practical look at the concepts and techniques needed to get the most out of your data

Author : Jiawei Han,Micheline Kamber,Jian Pei
Publisher : Elsevier
Page : 740 pages
File Size : 50,6 Mb
Release : 2011-06-09
Category : Computers
ISBN : 9780123814807

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Data Mining: Concepts and Techniques by Jiawei Han,Micheline Kamber,Jian Pei Pdf

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. 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

Author : Anonim
Publisher : BoD – Books on Demand
Page : 226 pages
File Size : 44,5 Mb
Release : 2022-03-30
Category : Computers
ISBN : 9781839692666

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Data Mining by Anonim Pdf

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining.

Author : Dolf Zantinge
Publisher : Unknown
Page : 158 pages
File Size : 51,8 Mb
Release : 2002
Category : Data mining
ISBN : OCLC:778190813

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Data Mining by Dolf Zantinge Pdf

Author : Graham Williams
Publisher : Springer Science & Business Media
Page : 374 pages
File Size : 43,9 Mb
Release : 2011-08-04
Category : Mathematics
ISBN : 9781441998903

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Data Mining with Rattle and R by Graham Williams Pdf

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

Author : Pang-Ning Tan,Sanjay Chawla,Chin Kuan Ho,James Bailey
Publisher : Springer
Page : 445 pages
File Size : 45,6 Mb
Release : 2012-05-10
Category : Computers
ISBN : 9783642302206

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Advances in Knowledge Discovery and Data Mining, Part II by Pang-Ning Tan,Sanjay Chawla,Chin Kuan Ho,James Bailey Pdf

The two-volume set LNAI 7301 and 7302 constitutes the refereed proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in May 2012. The total of 20 revised full papers and 66 revised short papers were carefully reviewed and selected from 241 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas. The papers are organized in topical sections on supervised learning: active, ensemble, rare-class and online; unsupervised learning: clustering, probabilistic modeling in the first volume and on pattern mining: networks, graphs, time-series and outlier detection, and data manipulation: pre-processing and dimension reduction in the second volume.

Author : Trivedi, Shrawan Kumar,Dey, Shubhamoy,Kumar, Anil,Panda, Tapan Kumar
Publisher : IGI Global
Page : 438 pages
File Size : 54,8 Mb
Release : 2017-02-14
Category : Computers
ISBN : 9781522520320

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Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence by Trivedi, Shrawan Kumar,Dey, Shubhamoy,Kumar, Anil,Panda, Tapan Kumar Pdf

The development of business intelligence has enhanced the visualization of data to inform and facilitate business management and strategizing. By implementing effective data-driven techniques, this allows for advance reporting tools to cater to company-specific issues and challenges. The Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence is a key resource on the latest advancements in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining, this publication is an ideal reference source for research scholars, management faculty, and practitioners.

Author : 韩家炜
Publisher : Unknown
Page : 0 pages
File Size : 52,8 Mb
Release : 2023
Category : Data mining
ISBN : OCLC:1351026047

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Data Mining by 韩家炜 Pdf

Author : Jiawei Han
Publisher : Morgan Kaufmann
Page : 550 pages
File Size : 46,6 Mb
Release : 2001
Category : Computers
ISBN : 1558604898

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Data Mining by Jiawei Han Pdf

Data warehouse and OLAP technology for data mining. Data preprocessing. Data mining primitives, languages, and system architecture. Concept description: characterization and comparison. Mining association rules in large databases. Classification and prediction. Cluster analysis. Mining complex types of data. Applications and trends in data mining. Appendix.

Author : Usman, Muhammad
Publisher : IGI Global
Page : 392 pages
File Size : 47,8 Mb
Release : 2015-08-03
Category : Computers
ISBN : 9781466685147

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Improving Knowledge Discovery through the Integration of Data Mining Techniques by Usman, Muhammad Pdf

Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing technologies have similar objectives and can potentially benefit from each other’s methods to facilitate knowledge discovery. Improving Knowledge Discovery through the Integration of Data Mining Techniques provides insight concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. Decision makers, academicians, researchers, advanced-level students, technology developers, and business intelligence professionals will find this book useful in furthering their research exposure to relevant topics in knowledge discovery.

Author : R. Boire
Publisher : Springer
Page : 242 pages
File Size : 49,8 Mb
Release : 2014-11-17
Category : Business & Economics
ISBN : 9781137406194

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Data Mining for Managers by R. Boire Pdf

Big Data is a growing business trend, but there little advice available on how to use it practically. Written by a data mining expert with over 30 years of experience, this book uses case studies to help marketers, brand managers and IT professionals understand how to capture and measure data for marketing purposes.

Author : Petra Perner
Publisher : Springer
Page : 454 pages
File Size : 40,7 Mb
Release : 2015-06-30
Category : Computers
ISBN : 9783319210247

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Machine Learning and Data Mining in Pattern Recognition by Petra Perner Pdf

This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015, held in Hamburg, Germany in July 2015. The 41 full papers presented were carefully reviewed and selected from 123 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.

Author : Prasad S. Thenkabail,John G. Lyon,Alfredo Huete
Publisher : CRC Press
Page : 612 pages
File Size : 47,7 Mb
Release : 2018-12-07
Category : Technology & Engineering
ISBN : 9781351673280

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Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation by Prasad S. Thenkabail,John G. Lyon,Alfredo Huete Pdf

Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the spectrum from various ground-based, airborne, and spaceborne platforms. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors’ perspective. Key Features of Volume I: Provides the fundamentals of hyperspectral remote sensing used in agricultural crops and vegetation studies. Discusses the latest advances in hyperspectral remote sensing of ecosystems and croplands. Develops online hyperspectral libraries, proximal sensing and phenotyping for understanding, modeling, mapping, and monitoring crop and vegetation traits. Implements reflectance spectroscopy of soils and vegetation. Enumerates hyperspectral data mining and data processing methods, approaches, and machine learning algorithms. Explores methods and approaches for data mining and overcoming data redundancy; Highlights the advanced methods for hyperspectral data processing steps by developing or implementing appropriate algorithms and coding the same for processing on a cloud computing platform like the Google Earth Engine. Integrates hyperspectral with other data, such as the LiDAR data, in the study of vegetation. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling.