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Data Mining: Practical Machine Learning Tools and Techniques
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TLDR
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, visualizationread more
Citations
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Journal ArticleDOI
A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.
Khabat Khosravi,Binh Thai Pham,Kamran Chapi,Ataollah Shirzadi,Himan Shahabi,Inge Revhaug,Indra Prakash,Dieu Tien Bui +7 more
TL;DR: Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively.
Patent
Modular backup and retrieval system used in conjunction with a storage area network
TL;DR: In this article, a modular computer storage system and method is provided for managing and directing data archiving functions, which is scalable and comprehends various storage media as well as diverse operating systems on a plurality of client devices.
Book ChapterDOI
The SPMF Open-Source Data Mining Library Version 2
Philippe Fournier-Viger,Jerry Chun-Wei Lin,Antonio Gomariz,Ted Gueniche,Azadeh Soltani,Zhi-Hong Deng,Hoang Thanh Lam +6 more
TL;DR: This paper introduces the second major revision of SPMF 2, which provides more than 60 new algorithm implementations (including novel algorithms for sequence prediction), an improved user interface with pattern visualization, a novel plug-in system, improved performance, and support for text mining.
Proceedings ArticleDOI
Inferring the source of encrypted HTTP connections
TL;DR: This work examines the effectiveness of two traffic analysis techniques, based upon classification algorithms, for identifying encrypted HTTP streams, and gives evidence that these techniques will exhibit the scalability necessary to be effective on the Internet.
Journal ArticleDOI
A survey of paraphrasing and textual entailment methods
TL;DR: Key ideas from the two areas of paraphrasing and textual entailment are summarized by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.
References
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Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
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.
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An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
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.
Journal ArticleDOI
A Coefficient of agreement for nominal Scales
TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.