[PDF] Learning Classification Algorithms In Data Mining eBook

Author : Xin-She Yang
Publisher : Academic Press
Page : 188 pages
File Size : 43,34 MB
Release : 2019-06-17
Category : Mathematics
ISBN : 0128172177

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Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

Author : Charu C. Aggarwal
Publisher : CRC Press
Page : 710 pages
File Size : 37,52 MB
Release : 2014-07-25
Category : Business & Economics
ISBN : 1498760589

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Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi

Author : Mohammed J. Zaki
Publisher : Cambridge University Press
Page : 779 pages
File Size : 19,52 MB
Release : 2020-01-30
Category : Business & Economics
ISBN : 1108473989

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

Author : Swetha Rajendiran
Publisher :
Page : 154 pages
File Size : 45,11 MB
Release : 2015
Category :
ISBN :

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Classification algorithms are used in data mining to classify data based on class labels. It involves building a model using training data set, and then using the built model to assign given items to specific classes/categories. In the model building process, also called training process, a classification algorithm finds relationships between the attributes of the data and the target. Different classification algorithms use different techniques for finding relationships. These relationships are summarized in a model, which can then be applied to a new data set in which the class assignments are unknown. This project's objective is to create a courseware that focuses on creating materials to achieve the goal of helping the students get deeper understanding of the most used classification algorithms in data mining. The existing materials on the classification algorithms are completely textual and students find it difficult to grasp. By using interactive examples and animated tutorials provided in the courseware, students should be able to intuitively learn these classification algorithms easily. With the help of this courseware, students will be able to learn the algorithms using flash animations and then visualize the steps with the help of interactive examples that can be modified in many ways by the student to get a complete understanding of the algorithms. There is also information provided on how to make practical use of these algorithms using data mining tools such as Weka and RapidMiner where students can apply the algorithms on real datasets available. Implementation of the courseware is done with technologies such as HTML, JavaScript, and Bootstrap CSS.

Author : Charu C. Aggarwal
Publisher : CRC Press
Page : 704 pages
File Size : 50,46 MB
Release : 2014-07-25
Category : Business & Economics
ISBN : 1466586753

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Comprehensive Coverage of the Entire Area of Classification Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.

Author : Ian H. Witten
Publisher : Elsevier
Page : 665 pages
File Size : 30,63 MB
Release : 2011-02-03
Category : Computers
ISBN : 0080890369

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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

Author : Mohammed J. Zaki
Publisher : Cambridge University Press
Page : 607 pages
File Size : 11,60 MB
Release : 2014-05-12
Category : Computers
ISBN : 0521766338

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

Author : Pavel Brazdil
Publisher : Springer Science & Business Media
Page : 182 pages
File Size : 38,61 MB
Release : 2008-11-26
Category : Computers
ISBN : 3540732624

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Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.

Author : Xindong Wu
Publisher : CRC Press
Page : 230 pages
File Size : 41,69 MB
Release : 2009-04-09
Category : Business & Economics
ISBN : 142008965X

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Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is wri

Author : Oded Z Maimon
Publisher : World Scientific
Page : 328 pages
File Size : 16,92 MB
Release : 2014-09-03
Category : Computers
ISBN : 9814590096

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Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer: