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

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

Data Mining: sensible computer studying instruments and Techniques bargains an intensive grounding in computer studying strategies in addition to useful recommendation on utilising computer studying instruments and strategies in real-world info mining events. This hugely expected 3rd version of the main acclaimed paintings on info mining and computer studying will train you every 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 replicate the technical adjustments and modernizations that experience taken position within the box because the final variation, together with new fabric on facts ameliorations, Ensemble studying, tremendous information units, Multi-instance studying, plus a brand new model of the preferred Weka computing device studying software program constructed via the authors. Witten, Frank, and corridor contain either tried-and-true strategies of at the present time in addition to equipment on the innovative of up to date learn.

*Provides a radical grounding in desktop studying techniques in addition to functional recommendation on utilising the instruments and strategies for your information mining tasks *Offers concrete suggestions and methods for functionality development that paintings through reworking the enter or output in computer studying equipment *Includes downloadable Weka software program toolkit, a suite of laptop studying algorithms for facts mining tasks-in an up-to-date, interactive interface. Algorithms in toolkit conceal: info pre-processing, class, regression, clustering, organization principles, visualization

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

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5 shows some data for which both the outcome and the attributes are numeric. It concerns the relative performance of computer processing power on the basis of a number of relevant attributes; each row represents one of 209 different computer configurations. ) This is called a regression equation, and the process of determining the weights is called regression, a well-known procedure in statistics that we will review in Chapter 4. 4), and in Chapter 3 we will examine different representations that can be used for predicting numeric quantities.

The definition sounds circular, but it can be made to work. Search engines use PageRank (among other things) to sort web pages into order before displaying the results of your search. Another way in which search engines tackle the problem of how to rank web pages is to use machine learning based on a training set of example queries— documents that contain the terms in the query and human judgments about how relevant the documents are to that query. Then a learning algorithm analyzes this training data and comes up with a way to predict the relevance judgment for any 21 22 CHAPTER 1 What’s It All About?

A metric called PageRank, introduced by Google’s founders and also used in various guises by other search engine developers, attempts to measure the standing of a web page. The more pages that link to your web site, the higher its prestige, especially if the pages that link in have high prestige themselves. The definition sounds circular, but it can be made to work. Search engines use PageRank (among other things) to sort web pages into order before displaying the results of your search. Another way in which search engines tackle the problem of how to rank web pages is to use machine learning based on a training set of example queries— documents that contain the terms in the query and human judgments about how relevant the documents are to that query.

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