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

By Lior Rokach

Determination bushes became probably the most strong and well known ways in wisdom discovery and information mining; it's the technology of exploring huge and complicated our bodies of knowledge in an effort to realize important styles. choice tree studying keeps to conform through the years. current tools are always being better and new tools introduced.

This 2d version is devoted fullyyt to the sphere of determination timber in facts mining; to hide all facets of this crucial strategy, in addition to greater or new tools and strategies constructed after the book of our first version. during this re-creation, all chapters were revised and new subject matters introduced in. New issues contain Cost-Sensitive energetic studying, studying with doubtful and Imbalanced facts, utilizing selection bushes past type initiatives, privateness protecting selection Tree studying, classes discovered from Comparative stories, and studying choice bushes for giant facts. A walk-through advisor to present open-source info mining software program can be integrated during this edition.

This ebook invitations readers to discover the numerous merits in information mining that call timber offer:

  • Self-explanatory and straightforward to stick with while compacted
  • Able to address a number of enter info: nominal, numeric and textual
  • Scales good to important data
  • Able to technique datasets that could have error or lacking values
  • High predictive functionality for a comparatively small computational effort
  • Available in lots of open resource info mining applications over numerous platforms
  • Useful for numerous initiatives, reminiscent of category, regression, clustering and have selection
    • Readership: Researchers, graduate and undergraduate scholars in details platforms, engineering, desktop technology, facts and administration.

Show description

Read or Download Data Mining With Decision Trees: Theory and Applications (2nd Edition) PDF

Similar data mining books

Mining of Massive Datasets

The recognition of the internet and web trade offers many tremendous huge datasets from which details might be gleaned by means of info mining. This booklet makes a speciality of sensible algorithms which have been used to unravel key difficulties in information mining and that are used on even the most important datasets. It starts with a dialogue of the map-reduce framework, a big software for parallelizing algorithms immediately.

Twitter Data Analytics (SpringerBriefs in Computer Science)

This short presents tools for harnessing Twitter information to find options to complicated inquiries. The short introduces the method of gathering information via Twitter’s APIs and provides innovations for curating huge datasets. The textual content offers examples of Twitter facts with real-world examples, the current demanding situations and complexities of establishing visible analytic instruments, and the simplest thoughts to handle those matters.

Advances in Natural Language Processing: 9th International Conference on NLP, PolTAL 2014, Warsaw, Poland, September 17-19, 2014. Proceedings

This e-book constitutes the refereed lawsuits of the ninth foreign convention on Advances in common Language Processing, PolTAL 2014, Warsaw, Poland, in September 2014. The 27 revised complete papers and 20 revised brief papers provided have been conscientiously reviewed and chosen from eighty three submissions. The papers are prepared in topical sections on morphology, named entity reputation, time period extraction; lexical semantics; sentence point syntax, semantics, and desktop translation; discourse, coreference answer, automated summarization, and query answering; textual content category, info extraction and data retrieval; and speech processing, language modelling, and spell- and grammar-checking.

Analysis of Large and Complex Data

This booklet bargains a photograph of the state of the art in category on the interface among information, laptop technological know-how and alertness fields. The contributions span a large spectrum, from theoretical advancements to functional functions; all of them proportion a robust computational part. the subjects addressed are from the subsequent fields: data and information research; computing device studying and data Discovery; info research in advertising and marketing; info research in Finance and Economics; info research in medication and the existence Sciences; information research within the Social, Behavioural, and overall healthiness Care Sciences; information research in Interdisciplinary domain names; category and topic Indexing in Library and knowledge technological know-how.

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

Example text

1 illustrates a typical precision-recall graph. This twodimensional graph is closely related to the well-known receiver operating characteristics (ROC) graphs in which the true positive rate (recall) is plotted on the Y -axis and the false positive rate is plotted on the X-axis [Ferri et al. (2002)]. However, unlike the precision-recall graph, the ROC diagram is always convex. Given a probabilistic classifier, this trade-off graph may be obtained by setting different threshold values. 5. 5, the trade-off graph can be obtained.

These techniques include the most common methods of traditional statistics, like the goodness-of-fit test, the t-test of means and analysis of variance. These methods are not as much related to data mining as are their discovery-oriented counterparts because most data mining problems are concerned with selecting a hypothesis (out of a set of hypotheses) rather than testing a known one. While one of the main objectives of data mining is model identification, statistical methods usually focus on model estimation [Elder and Pregibon (1996)].

4 A typical ROC curve. 2 19:12 Data Mining with Decision Trees (2nd Edition) - 9in x 6in b1856-ch04 Data Mining with Decision Trees Hit-Rate Curve The hit-rate curve presents the hit ratio as a function of the quota size. Hitrate is calculated by counting the actual positive labeled instances inside a determined quota [An and Wang (2001)]. 10) where t[k] represents the truly expected outcome of the instance located in the k’th position when the instances are sorted according to their conditional probability for “positive” by descending order.

Download PDF sample

Rated 4.96 of 5 – based on 47 votes