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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
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Journal ArticleDOI
Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems
TL;DR: An advanced architecture for a personalization system to facilitate Web mining is proposed and the meaning of several recommendations are described, starting from the rules discovered by the Web mining algorithms.
Book ChapterDOI
Information Extraction: Past, Present and Future
Jakub Piskorski,Roman Yangarber +1 more
TL;DR: Although the targeted nature of this task makes it more tractable than some of the more open-ended tasks in NLP, it is replete with challenges as the information landscape evolves, which also makes it an exciting research subject.
Journal ArticleDOI
Integrated analysis of established and novel microbial and chemical methods for microbial source tracking.
Anicet R. Blanch,Lluís A. Belanche-Muñoz,Xavier Bonjoch,James Ebdon,Christophe Gantzer,Francisco Lucena,Jakob Ottoson,Christos Kourtis,Aina Iversen,Inger Kühn,Laura Moce,Maite Muniesa,J. Schwartzbrod,Sylvain Skraber,Georgios T. Papageorgiou,Huw Taylor,J.L. Wallis,Joan Jofre +17 more
TL;DR: Several statistical or machine learning methods were evaluated and provided two successful predictive models based on just two variables, giving 100% correct classification: the ratio of the densities of somatic coliphages and phages infecting Bacteroides thetaiotaomicron to the density of somatics coliphage and the ratio
Journal ArticleDOI
Detecting malicious tweets in trending topics using a statistical analysis of language
TL;DR: This paper presents the first work that tries to detect spam tweets in real time using language as the primary tool and develops a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks.
Journal ArticleDOI
RNABindR: a server for analyzing and predicting RNA-binding sites in proteins.
Michael Terribilini,Jeffry D. Sander,Jae-Hyung Lee,Peter Zaback,Robert L. Jernigan,Vasant Honavar,Drena Dobbs +6 more
TL;DR: RNABindR is a web-based server that identifies and displays RNA-binding residues in known protein–RNA complexes and predicts RNA- binding residues in proteins of unknown structure and automatically displays ‘high specificity’ and ‘ high sensitivity’ predictions of RNA- Binding residues.
References
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TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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