Download Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten PDF

By Ian H. Witten

This publication bargains a radical grounding in computing device studying strategies in addition to sensible suggestion on utilising computing device studying instruments and methods in real-world information mining events. inside of, you are going to examine all you want to find out about getting ready inputs, reading outputs, comparing effects, and the algorithmic tools on the middle of profitable facts miningincluding either tried-and-true thoughts of the previous and Java-based tools on the innovative of latest study. if you are concerned at any point within the paintings of extracting usable wisdom from huge collections of information, this sincerely written and successfully illustrated ebook will turn out a useful resource.Complementing the authors' guide is a completely practical platform-independent Java software program approach for computer studying, on hand for obtain. use it on the pattern info units supplied to refine your info mining abilities, use it on your personal facts to figure significant styles and generate beneficial insights, adapt it to your really expert information mining functions, or use it to strengthen your individual computer studying schemes.* is helping you choose acceptable methods to specific difficulties and to check and evaluation the result of diversified techniques.* Covers functionality development concepts, together with enter preprocessing and mixing output from diverse methods.* Comes with downloadable desktop studying software program: use it to grasp the recommendations lined inside of, use it on your personal tasks, and/or customise it to satisfy precise wishes.

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Extra resources for Data Mining: Practical Machine Learning Tools and Techniques With Java Implementations

Example text

The main() method accepts the following command-line arguments: the name of a message file (given by –m), the name of a file holding an object of class MessageClassifier (–t) and, optionally, the classification of the message (–c). The message’s class can be hit or miss. If the user provides a classification using –c, the message will be added to the training data; if not, the program will classify the message as either hit or miss. Main() The main() method reads the message into an array of characters and checks whether the user has provided a classification for it.

There are two types of evaluators that you can specify with –E: ones that consider one attribute at a time, and ones that consider sets of attributes together. attributeSelection. attributeSelection. InfoGainAttributeEval, which evaluates attributes according to their information gain. attributeSelection. CfsSubsetEval, which evaluates subsets of features by the correlation among them. If you give the name of a subclass of AttributeEvaluator, you must also provide, using –T, a threshold by which the filter can discard low-scoring attributes.

By default all instances are deleted that exhibit one of a given set of nominal attribute values (if the specified attribute is nominal), or a numeric value below a given threshold (if it is numeric). However, the matching criterion can be inverted using –V. The SwapAttributeValuesFilter is a simple one: all it does is swap the positions of two values of a nominal attribute. Of course, this could also be accomplished by editing the ARFF file in a word processor. The order of attribute values is entirely cosmetic: it does not affect machine learning at all.

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