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Book

Data Mining: Practical Machine Learning Tools and Techniques

25 Oct 1999-

TL;DR: 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.
Abstract: Data Mining: Practical Machine Learning Tools and Techniques 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. *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
Citations
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Book
Jiawei Han1, Micheline Kamber2, Jian Pei2Institutions (2)
08 Sep 2000-
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always 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. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. 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. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * 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 real business data

23,590 citations


Journal ArticleDOI
Mark Hall, Eibe Frank1, Geoffrey Holmes1, Bernhard Pfahringer1  +2 moreInstitutions (1)
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Abstract: More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.

18,835 citations


Journal ArticleDOI
Wei-Yin Loh1Institutions (1)
TL;DR: This article gives an introduction to the subject of classification and regression trees by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.
Abstract: Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8 This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Prediction Technologies > Statistical Fundamentals

14,798 citations


Journal ArticleDOI
Yvan Saeys1, Iñaki Inza1, Pedro Larrañaga1Institutions (1)
10 Sep 2007-Bioinformatics
TL;DR: A basic taxonomy of feature selection techniques is provided, providing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.
Abstract: Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications. Contact: yvan.saeys@psb.ugent.be Supplementary information: http://bioinformatics.psb.ugent.be/supplementary_data/yvsae/fsreview

4,232 citations


Cites methods from "Data Mining: Practical Machine Lear..."

  • ...On this website, the publications are indexed according to the FS technique used, a number of keywords accompanying each reference to understand its FS methodological contributions....

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  • ...Software for feature selection General purpose FS software WEKA Java Witten and Frank (2005) http://www.cs.waikato.ac.nz/ml/weka Fast Correlation Based Filter Java Yu and Liu (2004) http://www.public.asu.edu/ huanliu/FCBF/FCBFsoftware.html Feature Selection Book Ansi C Liu and Motoda (1998)…...

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  • ...Software for feature selection General purpose FS software WEKA Java Witten and Frank (2005) http://www.cs.waikato.ac.nz/ml/weka Fast Correlation Based Filter Java Yu and Liu (2004) http://www.public.asu.edu/ huanliu/FCBF/FCBFsoftware.html Feature Selection Book Ansi C Liu and Motoda (1998) http://www.public.asu.edu/ huanliu/Fsbook MLCþþ Cþþ Kohavi et al. (1996) http://www.sgi.com/tech/mlc Spider Matlab – http://www.kyb.tuebingen.mpg.de/bs/people/spider SVM and Kernel Methods Matlab Canu et al. (2003) http://asi.insa-rouen.fr/ arakotom/toolbox/index Matlab Toolbox Microarray analysis FS software SAM R, Excel Tusher et al. (2001) http://www-stat.stanford.edu/ tibs/SAM/ GALGO R Trevino and Falciani (2006) http://www.bip.bham.ac.uk/bioinf/galgo.html PCP C, Cþþ Buturovic (2005) http://pcp.sourceforge.net GA-KNN C Li et al. (2001) http://dir.niehs.nih.gov/microarray/datamining/ Rankgene C Su et al. (2003) http://genomics10.bu.edu/yangsu/rankgene/ EDGE R Leek et al. (2006) http://www.biostat.washington.edu/software/jstorey/edge/ GEPAS-Prophet Perl, C Medina et al. (2007) http://prophet.bioinfo.cipf.es/ DEDS (Bioconductor) R Yang et al. (2005) http://www.bioconductor.org/ RankProd (Bioconductor) R Breitling et al. (2004) http://www.bioconductor.org/ Limma (Bioconductor) R Smyth (2004) http://www.bioconductor.org/ Multtest (Bioconductor) R Dudoit et al. (2003) http://www.bioconductor.org/ Nudge (Bioconductor) R Dean and Raftery (2005) http://www.bioconductor.org/ Qvalue (Bioconductor) R Storey (2002) http://www.bioconductor.org/ twilight (Bioconductor) R Scheid and Spang (2005) http://www.bioconductor.org/ ComparativeMarkerSelection JAVA, R Gould et al. (2006) http://www.broad.mit.edu/genepattern (GenePattern) Mass spectra analysis FS software GA-KNN C Li et al. (2004) http://dir.niehs.nih.gov/microarray/datamining/ R-SVM R, C, Cþþ Zhang et al. (2006) http://www.hsph.harvard.edu/bioinfocore/RSVMhome/ R-SVM.html SNP analysis FS software CHOISS Cþþ, Perl Lee and Kang (2004) http://biochem.kaist.ac.kr/choiss.htm MLR-tagging C He and Zelikovsky (2006) http://alla.cs.gsu.ed/ software/tagging/tagging.html WCLUSTAG JAVA Sham et al. (2007) http://bioinfo.hku.hk/wclustag D ow nloaded from https://academ ic.oup.com /bioinform atics/article/23/19/2507/185254 by guest on 08 M arch 2022 discusses the use of feature selection for a document classification task....

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Book ChapterDOI
Ling Bao1, Stephen S. Intille1Institutions (1)
21 Apr 2004-
TL;DR: This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves, and suggests that multiple accelerometers aid in recognition.
Abstract: In this work, algorithms are developed and evaluated to de- tect physical activities from data acquired using five small biaxial ac- celerometers worn simultaneously on different parts of the body. Ac- celeration data was collected from 20 subjects without researcher su- pervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. De- cision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers - thigh and wrist - the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves.

3,075 citations


References
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Book
01 Sep 1988-
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

52,793 citations


Book
Vladimir Vapnik1Institutions (1)
01 Jan 1995-
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

38,164 citations


Book
Bradley Efron1, Robert TibshiraniInstitutions (1)
01 Jan 1993-
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Abstract: This article presents bootstrap methods for estimation, using simple arguments. Minitab macros for implementing these methods are given.

36,497 citations


Journal ArticleDOI
Corinna Cortes1, Vladimir Vapnik1Institutions (1)
15 Sep 1995-Machine Learning
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

35,157 citations


01 Jan 1989-
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

32,374 citations


Network Information
Related Papers (5)
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Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
202216
2021650
2020940
20191,107
20181,065
20171,010