Data Preprocessing In Data Mining

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Data Preprocessing in Data Mining

Salvador García,Julián Luengo,Francisco Herrera
Data Preprocessing in Data Mining Book PDF

Author : Salvador García,Julián Luengo,Francisco Herrera
Publisher : Springer
Page : 320 pages
File Size : 52,8 Mb
Release : 2014-08-30
Category : Technology & Engineering
ISBN : 9783319102474

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Data Preprocessing in Data Mining by Salvador García,Julián Luengo,Francisco Herrera Pdf

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Data Preprocessing in Data Mining

Salvador García,Julián Luengo,Francisco Herrera
Data Preprocessing in Data Mining Book PDF

Author : Salvador García,Julián Luengo,Francisco Herrera
Publisher : Springer
Page : 0 pages
File Size : 49,7 Mb
Release : 2016-09-10
Category : Technology & Engineering
ISBN : 3319377310

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Data Preprocessing in Data Mining by Salvador García,Julián Luengo,Francisco Herrera Pdf

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Data Mining: Know It All

Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau
Data Mining Know It All Book PDF

Author : Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau
Publisher : Morgan Kaufmann
Page : 477 pages
File Size : 47,6 Mb
Release : 2008-10-31
Category : Computers
ISBN : 9780080877884

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Data Mining: Know It All by Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau Pdf

This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints. Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader’s technical expertise and ability to implement practical solutions. Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.

Data Mining: Concepts and Techniques

Jiawei Han,Micheline Kamber,Jian Pei
Data Mining Concepts and Techniques Book PDF

Author : Jiawei Han,Micheline Kamber,Jian Pei
Publisher : Elsevier
Page : 740 pages
File Size : 51,6 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

Data Preparation for Data Mining

Dorian Pyle
Data Preparation for Data Mining Book PDF

Author : Dorian Pyle
Publisher : Morgan Kaufmann
Page : 566 pages
File Size : 46,5 Mb
Release : 1999-03-22
Category : Computers
ISBN : 1558605290

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Data Preparation for Data Mining by Dorian Pyle Pdf

This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.

Data Mining

Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski,Lukasz Andrzej Kurgan
Data Mining Book PDF

Author : Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski,Lukasz Andrzej Kurgan
Publisher : Springer Science & Business Media
Page : 606 pages
File Size : 47,8 Mb
Release : 2007-10-05
Category : Computers
ISBN : 9780387367958

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Data Mining by Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski,Lukasz Andrzej Kurgan Pdf

This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.

Machine Learning and Big Data

Uma N. Dulhare,Khaleel Ahmad,Khairol Amali Bin Ahmad
Machine Learning and Big Data Book PDF

Author : Uma N. Dulhare,Khaleel Ahmad,Khairol Amali Bin Ahmad
Publisher : John Wiley & Sons
Page : 544 pages
File Size : 48,7 Mb
Release : 2020-09-01
Category : Computers
ISBN : 9781119654742

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Machine Learning and Big Data by Uma N. Dulhare,Khaleel Ahmad,Khairol Amali Bin Ahmad Pdf

This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.

Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

Rana, Dipti P.,Mehta, Rupa G.
Data Preprocessing Active Learning and Cost Perceptive Approaches for Resolving Data Imbalance Book PDF

Author : Rana, Dipti P.,Mehta, Rupa G.
Publisher : IGI Global
Page : 309 pages
File Size : 53,7 Mb
Release : 2021-06-04
Category : Computers
ISBN : 9781799873730

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Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance by Rana, Dipti P.,Mehta, Rupa G. Pdf

Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

Data Mining and Data Warehousing

Parteek Bhatia
Data Mining and Data Warehousing Book PDF

Author : Parteek Bhatia
Publisher : Cambridge University Press
Page : 513 pages
File Size : 55,5 Mb
Release : 2019-06-27
Category : Computers
ISBN : 9781108727747

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Data Mining and Data Warehousing by Parteek Bhatia Pdf

Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.

Machine Learning and Data Mining

Igor Kononenko,Matjaz Kukar
Machine Learning and Data Mining Book PDF

Author : Igor Kononenko,Matjaz Kukar
Publisher : Horwood Publishing
Page : 484 pages
File Size : 46,5 Mb
Release : 2007-04-30
Category : Computers
ISBN : 1904275214

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Machine Learning and Data Mining by Igor Kononenko,Matjaz Kukar Pdf

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 Methods for Knowledge Discovery

Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski
Data Mining Methods for Knowledge Discovery Book PDF

Author : Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski
Publisher : Springer Science & Business Media
Page : 508 pages
File Size : 51,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461555896

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Data Mining Methods for Knowledge Discovery by Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski Pdf

Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography. Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.

Big Data Preprocessing

Julián Luengo,Diego García-Gil,Sergio Ramírez-Gallego,Salvador García,Francisco Herrera
Big Data Preprocessing Book PDF

Author : Julián Luengo,Diego García-Gil,Sergio Ramírez-Gallego,Salvador García,Francisco Herrera
Publisher : Springer Nature
Page : 193 pages
File Size : 45,8 Mb
Release : 2020-03-16
Category : Computers
ISBN : 9783030391058

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Big Data Preprocessing by Julián Luengo,Diego García-Gil,Sergio Ramírez-Gallego,Salvador García,Francisco Herrera Pdf

This book offers a comprehensible overview of Big Data Preprocessing, which includes a formal description of each problem. It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud. Finally, this book provides some novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing techniques applied and some other open problems. Practitioners and data scientists who work in this field, and want to introduce themselves to preprocessing in large data volume scenarios will want to purchase this book. Researchers that work in this field, who want to know which algorithms are currently implemented to help their investigations, may also be interested in this book.

Data Mining

Ian H. Witten,Eibe Frank,Mark A. Hall
Data Mining Book PDF

Author : Ian H. Witten,Eibe Frank,Mark A. Hall
Publisher : Elsevier
Page : 665 pages
File Size : 43,5 Mb
Release : 2011-02-03
Category : Computers
ISBN : 9780080890364

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Data Mining by Ian H. Witten,Eibe Frank,Mark A. Hall Pdf

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Discovering Knowledge in Data

Daniel T. Larose
Discovering Knowledge in Data Book PDF

Author : Daniel T. Larose
Publisher : John Wiley & Sons
Page : 240 pages
File Size : 48,5 Mb
Release : 2005-01-28
Category : Computers
ISBN : 9780471687535

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Discovering Knowledge in Data by Daniel T. Larose Pdf

Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.

Data Mining and Decision Support

Dunja Mladenic,Nada Lavrač,Marko Bohanec,Steve Moyle
Data Mining and Decision Support Book PDF

Author : Dunja Mladenic,Nada Lavrač,Marko Bohanec,Steve Moyle
Publisher : Springer Science & Business Media
Page : 284 pages
File Size : 49,8 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461502869

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Data Mining and Decision Support by Dunja Mladenic,Nada Lavrač,Marko Bohanec,Steve Moyle Pdf

Data mining deals with finding patterns in data that are by user-definition, interesting and valid. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, visualization and others. Decision support focuses on developing systems to help decision-makers solve problems. Decision support provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data warehouses. Independently, data mining and decision support are well-developed research areas, but until now there has been no systematic attempt to integrate them. Data Mining and Decision Support: Integration and Collaboration, written by leading researchers in the field, presents a conceptual framework, plus the methods and tools for integrating the two disciplines and for applying this technology to business problems in a collaborative setting.