Data Mining With Decision Trees: Theory and Applications by Lior Rokach

By Lior Rokach

Determination bushes became some of the most strong and well known ways in wisdom discovery and knowledge mining; it's the technological know-how of exploring huge and complicated our bodies of information to be able to notice priceless styles. choice tree studying keeps to conform through the years. current equipment are consistently being more desirable and new tools introduced.

This second variation is devoted completely to the sector of determination bushes in information mining; to hide all points of this crucial process, in addition to superior or new equipment and strategies built after the booklet of our first variation. during this new version, all chapters were revised and new issues introduced in. New subject matters contain Cost-Sensitive lively studying, studying with doubtful and Imbalanced facts, utilizing choice bushes past type projects, privateness protecting determination Tree studying, classes realized from Comparative reports, and studying choice bushes for large information. A walk-through advisor to latest open-source info mining software program is additionally integrated during this edition.

This ebook invitations readers to discover the various advantages in information mining that call timber offer:

  • Self-explanatory and straightforward to stick to whilst compacted
  • Able to address a number of enter information: nominal, numeric and textual
  • Scales good to important data
  • Able to technique datasets which may have error or lacking values
  • High predictive functionality for a comparatively small computational effort
  • Available in lots of open resource facts mining programs over numerous platforms
  • Useful for varied initiatives, akin to class, regression, clustering and have selection
    • Readership: Researchers, graduate and undergraduate scholars in details structures, engineering, machine technological know-how, records and administration.

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Extra info for Data Mining With Decision Trees: Theory and Applications (2nd Edition)

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In this book, a bag schema is denoted as B(A∪y) where A denotes the set of input attributes containing n attributes: A = {a1 , . . , ai , . . , an } and y represents the class variable or the target attribute. Attributes (sometimes called field, variable or feature) are typically one of two types: nominal (values are members of an unordered set), or numeric (values are real numbers). In attribute ai , it is useful to denote its domain values by dom(ai ) = {vi,1 , vi,2 , . . , vi,|dom(ai )| }, where 23 page 23 August 18, 2014 24 19:12 Data Mining with Decision Trees (2nd Edition) - 9in x 6in b1856-ch03 Data Mining with Decision Trees |dom(ai )| stands for its finite cardinality.

5 [Quinlan (1993)], CART [Breiman et al. (1984)]. 5 and CART). Other inducers perform only the growing phase. 1 presents a typical pseudo code for a top-down inducing algorithm of a decision tree using growing and pruning. Note that these page 28 August 18, 2014 19:12 Data Mining with Decision Trees (2nd Edition) - 9in x 6in b1856-ch03 A Generic Algorithm for Top-Down Induction of Decision Trees page 29 29 TreeGrowing (S,A,y,SplitCriterion,StoppingCriterion) Where: S - Training Set A - Input Feature Set y - Target Feature SplitCriterion --- the method for evaluating a certain split StoppingCriterion --- the criteria to stop the growing process Create a new tree T with a single root node.

Classification, regression, or clustering. This mostly depends on the goals and the previous steps. There are two major goals in Data Mining: prediction and description. Prediction is often referred to as supervised Data Mining, while descriptive Data Mining includes the unsupervised classification and visualization aspects of Data Mining. Most data mining techniques are based on inductive learning where a model is constructed explicitly or implicitly by generalizing from a sufficient number of training examples.

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