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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
Application of ant K-means on clustering analysis
TL;DR: A novel clustering method that modifies the K-means as locating the objects in a cluster with the probability, which is updated by the pheromone, is proposed, and the results indicated that the proposed method is the best among these three methods based on TWCV.
Journal ArticleDOI
XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring
TL;DR: In this article, an XGBoost algorithm-based prediction model is proposed for structural health monitoring, which considers all potential influential factors simultaneously, including concrete water/cement ratio and structure curing environment as well as their complex interrelationships.
Journal ArticleDOI
Simultaneous co-clustering and learning to address the cold start problem in recommender systems
TL;DR: A hybrid approach is presented that combines collaborative filtering recommendations with demographic information and provides a hybrid recommendation approach that can address the (pure) cold start problem, where no collaborative information is available for new users.
Journal ArticleDOI
Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning
TL;DR: The results show that churn prediction is a valuable strategy to identify and profile those customers at risk and the performance of the ensembles is more robust and better than the single models.
Book ChapterDOI
Extracting Landmarks with Data Mining Methods
TL;DR: The navigation task is a very demanding application for mobile users and the algorithms of present software solutions are based on the established methods of car navigation systems and thus exhibit some inherent disadvantages.
References
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Genetic algorithms in search, optimization, and machine learning
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The Nature of Statistical Learning Theory
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?
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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.
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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.