Data Mining  Concepts and Techniques Book
Score: 3.5
From 5 Ratings

Data Mining Concepts and Techniques


  • Author : Jiawei Han
  • Publisher : Elsevier
  • Release Date : 2011-06-09
  • Genre: Computers
  • Pages : 744
  • ISBN 10 : 0123814804

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Data Mining Concepts and Techniques Excerpt :

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

Advanced Data Mining Tools and Methods for Social Computing Book

Advanced Data Mining Tools and Methods for Social Computing


  • Author : Sourav De
  • Publisher : Academic Press
  • Release Date : 2022-01-28
  • Genre: Computers
  • Pages : 292
  • ISBN 10 : 9780323857093

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Advanced Data Mining Tools and Methods for Social Computing Excerpt :

Advanced Data Mining Tools and Methods for Social Computing explores advances in the latest data mining tools, methods, algorithms and the architectures being developed specifically for social computing and social network analysis. The book reviews major emerging trends in technology that are supporting current advancements in social networks, including data mining techniques and tools. It also aims to highlight the advancement of conventional approaches in the field of social networking. Chapter coverage includes reviews of novel techniques and state-of-the-art advances in the area of data mining, machine learning, soft computing techniques, and their applications in the field of social network analysis. Provides insights into the latest research trends in social network analysis Covers a broad range of data mining tools and methods for social computing and analysis Includes practical examples and case studies across a range of tools and methods Features coding examples and supplementary data sets in every chapter

Applied Data Mining Book

Applied Data Mining


  • Author : Guandong Xu
  • Publisher : CRC Press
  • Release Date : 2013-06-17
  • Genre: Computers
  • Pages : 284
  • ISBN 10 : 9781466585843

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Applied Data Mining Excerpt :

Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest advances in newly emerging information services. It explores the extension of well-studied algorithms and approaches into these new research arenas.

Data Mining and Machine Learning Book

Data Mining and Machine Learning


  • Author : Mohammed J. Zaki
  • Publisher : Cambridge University Press
  • Release Date : 2020-01-31
  • Genre: Business & Economics
  • Pages : 775
  • ISBN 10 : 9781108473989

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Data Mining and Machine Learning Excerpt :

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

Next Generation of Data Mining Book
Score: 4
From 1 Ratings

Next Generation of Data Mining


  • Author : Hillol Kargupta
  • Publisher : CRC Press
  • Release Date : 2008-12-24
  • Genre: Computers
  • Pages : 601
  • ISBN 10 : 1420085875

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Next Generation of Data Mining Excerpt :

Drawn from the US National Science Foundation’s Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms. The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.

Music Data Mining Book

Music Data Mining


  • Author : Tao Li
  • Publisher : CRC Press
  • Release Date : 2011-07-12
  • Genre: Business & Economics
  • Pages : 384
  • ISBN 10 : 9781439835524

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Music Data Mining Excerpt :

The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Bringing together an interdisciplinary array of top researchers, Music Data Mining presents a variety of approaches to successfully employ data mining techniques for the purpose of music processing. The book first covers music data mining tasks and algorithms and audio feature extraction, providing a framework for subsequent chapters. With a focus on data classification, it then describes a computational approach inspired by human auditory perception and examines instrument recognition, the effects of music on moods and emotions, and the connections between power laws and music aesthetics. Given the importance of social aspects in understanding music, the text addresses the use of the Web and peer-to-peer networks for both music data mining and evaluating music mining tasks and algorithms. It also discusses indexing with tags and explains how data can be collected using online human computation games. The final chapters offer a balanced exploration of hit song science as well as a look at symbolic musicology and data mining. The multifaceted nature of music information often requires algorithms and systems using sophisticated signal processing and machine learning techniques to better extract useful information. An excellent introduction to the field, this volume presents state-of-the-art techniques in music data mining and information retrieval to create novel ways of interacting with large music collections.

Machine Learning and Data Mining Book
Score: 3
From 1 Ratings

Machine Learning and Data Mining


  • Author : Igor Kononenko
  • Publisher : Horwood Publishing
  • Release Date : 2007-05-14
  • Genre: Computers
  • Pages : 454
  • ISBN 10 : 1904275214

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Machine Learning and Data Mining Excerpt :

Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.

Data Mining and Analysis Book

Data Mining and Analysis


  • Author : Mohammed J. Zaki
  • Publisher : Cambridge University Press
  • Release Date : 2014-05-12
  • Genre: Computers
  • Pages : 562
  • ISBN 10 : 9780521766333

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Data Mining and Analysis Excerpt :

A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

The Handbook of Data Mining Book

The Handbook of Data Mining


  • Author : Nong Ye
  • Publisher : CRC Press
  • Release Date : 2003-04-01
  • Genre: Computers
  • Pages : 720
  • ISBN 10 : 9781410607515

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The Handbook of Data Mining Excerpt :

Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The Handbook of Data Mining presents comprehensive coverage of data mining concepts and techniques. Algorithms, methodologies, management issues, and tools are all illustrated through engaging examples and real-world

R and Data Mining Book

R and Data Mining


  • Author : Yanchang Zhao
  • Publisher : Academic Press
  • Release Date : 2012-12-31
  • Genre: Mathematics
  • Pages : 256
  • ISBN 10 : 9780123972712

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R and Data Mining Excerpt :

R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work

Data Mining for Business Analytics Book

Data Mining for Business Analytics


  • Author : Galit Shmueli
  • Publisher : John Wiley & Sons
  • Release Date : 2019-10-14
  • Genre: Mathematics
  • Pages : 608
  • ISBN 10 : 9781119549857

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Data Mining for Business Analytics Excerpt :

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approach

Data Mining with R Book

Data Mining with R


  • Author : Luis Torgo
  • Publisher : CRC Press
  • Release Date : 2016-11-30
  • Genre: Business & Economics
  • Pages : 426
  • ISBN 10 : 9781315399096

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Data Mining with R Excerpt :

Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.

Data Mining and Data Visualization Book

Data Mining and Data Visualization


  • Author : Anonim
  • Publisher : Elsevier
  • Release Date : 2005-05-02
  • Genre: Mathematics
  • Pages : 800
  • ISBN 10 : 0080459404

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Data Mining and Data Visualization Excerpt :

Data Mining and Data Visualization focuses on dealing with large-scale data, a field commonly referred to as data mining. The book is divided into three sections. The first deals with an introduction to statistical aspects of data mining and machine learning and includes applications to text analysis, computer intrusion detection, and hiding of information in digital files. The second section focuses on a variety of statistical methodologies that have proven to be effective in data mining applications. These include clustering, classification, multivariate density estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses on data visualization and covers issues of visualization of high-dimensional data, novel graphical techniques with a focus on human factors, interactive graphics, and data visualization using virtual reality. This book represents a thorough cross section of internationally renowned thinkers who are inventing methods for dealing with a new data paradigm. Distinguished contributors who are international experts in aspects of data mining Includes data mining approaches to non-numerical data mining including text data, Internet traffic data, and geographic data Highly topical discussions reflecting current thinking on contemporary technical issues, e.g. streaming data Discusses taxonomy of dataset sizes, computational complexity, and scalability usually ignored in most discussions Thorough discussion of data visualization issues blending statistical, human factors, and computational insights

Data Mining Book

Data Mining


  • Author : Bhavani Thuraisingham
  • Publisher : CRC Press
  • Release Date : 2014-01-23
  • Genre: Computers
  • Pages : 288
  • ISBN 10 : 9781482252507

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Data Mining Excerpt :

Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges. Three parts divide Data Mining: Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision support, statistics, parallel processing, and architectural support for data mining Part II presents tools and techniques - getting the data ready, carrying out the mining, pruning the results, evaluating outcomes, defining specific approaches, examining a specific technique based on logic programming, and citing literature and vendors for up-to-date information Part III examines emerging trends - mining distributed and heterogeneous data sources; multimedia data, such as text, images, video; mining data on the World Wide Web; metadata aspects of mining; and privacy issues. This self-contained book also contains two appendices providing exceptional information on technologies, such as data management, and artificial intelligence. Is there a need for mining? Do you have the right tools? Do you have the people to do the work? Do you have sufficient funds allocated to the project? All these answers must be answered before embarking on a project. Data Mining provides singular guidance on appropriate applications for specific techniques as well as thoroughly assesses valuable product information.

Data Mining Book

Data Mining


  • Author : Nong Ye
  • Publisher : CRC Press
  • Release Date : 2013-07-26
  • Genre: Business & Economics
  • Pages : 349
  • ISBN 10 : 9781482219364

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Data Mining Excerpt :

New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various dat