Download Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank, Mark A. Hall PDF

By Ian H. Witten, Eibe Frank, Mark A. Hall

Data Mining: sensible computing device studying instruments and Techniques bargains a radical grounding in desktop studying thoughts in addition to sensible recommendation on utilizing desktop studying instruments and strategies in real-world facts mining occasions. This hugely expected 3rd version of the main acclaimed paintings on facts mining and computing device studying will train you every little thing you want to find out about getting ready inputs, studying outputs, comparing effects, and the algorithmic equipment on the center of winning information mining.

Thorough updates mirror the technical adjustments and modernizations that experience taken position within the box because the final variation, together with new fabric on info differences, Ensemble studying, titanic facts units, Multi-instance studying, plus a brand new model of the preferred Weka desktop studying software program built by means of the authors. Witten, Frank, and corridor comprise either tried-and-true strategies of this present day in addition to tools on the cutting edge of latest study.

*Provides an intensive grounding in computing device studying innovations in addition to sensible suggestion on utilising the instruments and strategies on your info mining initiatives *Offers concrete suggestions and methods for functionality development that paintings by way of reworking the enter or output in computer studying equipment *Includes downloadable Weka software program toolkit, a suite of computing device studying algorithms for info mining tasks-in an up-to-date, interactive interface. Algorithms in toolkit disguise: info pre-processing, class, regression, clustering, organization principles, visualization

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Extra resources for Data Mining: Practical Machine Learning Tools and Techniques (3rd Edition)

Sample text

2. Some good ones are If If If If temperature = cool humidity = normal and windy = false outlook = sunny and play = no windy = false and play = no then humidity = normal then play = yes then humidity = high then outlook = sunny and humidity = high All these rules are 100% correct on the given data; they make no false predictions. The first two apply to four examples in the dataset, the third to three examples, and the fourth to two examples. And there are many other rules. In fact, nearly 60 association rules can be found that apply to two or more examples of the weather data and are completely correct on this data.

We would like to mention in particular Rob Holte, Carl Gutwin, and Russell Beale, each of whom visited us for several months; David Aha, who although he only came for a few days did so at an early and fragile stage of the project and performed a great xxix xxx Acknowledgments service by his enthusiasm and encouragement; and Kai Ming Ting, who worked with us for two years on many of the topics described in Chapter 8 and helped to bring us into the mainstream of machine learning. More recent visitors include Arie BenDavid, Carla Brodley, and Stefan Kramer.

Data Mining Fortunately, the kind of learning techniques explained in this book do not present these conceptual problems—they are called machine learning without really presupposing any particular philosophical stance about what learning actually is. Data mining is a topic that involves learning in a practical, nontheoretical sense. We are interested in techniques for finding and describing structural patterns in data, as a tool for helping to explain that data and make predictions from it. The data will take the form of a set of examples, such as customers who have switched loyalties, for instance, or situations in which certain kinds of contact lenses can be prescribed.

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