Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery)

Luis Torgo

ISBN 10: 1439810184 / ISBN 13: 9781439810187

Edité par Chapman and Hall/CRC, 2010

A propos de cet article

Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

N° de réf. du vendeur M01439810184-G

Détails bibliographiques

• Titre: Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery)
• Éditeur: Chapman and Hall/CRC
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 1439810184
• ISBN à 13 chiffres: 9781439810187
• Reliure: Couverture rigide
• État de l'article: good

A propos de ce titre

Synopsis

The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data Mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining.

Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies:

2. Predicting algae blooms
3. Predicting stock market returns
4. Detecting fraudulent transactions
5. Classifying microarray samples

With these case studies, the author supplies all necessary steps, code, and data.

Web Resource
A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions.

À propos de l?auteur

Luis Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.