Download Data Mining with R: Learning with Case Studies (Chapman & by Luis Torgo PDF

By Luis Torgo

ISBN-10: 1439810184

ISBN-13: 9781439810187

The flexible services and big set of add-on programs make R a good substitute to many present and sometimes pricey info mining instruments. Exploring this region from the point of view of a practitioner, Data Mining with R: studying with Case Studies makes use of useful examples to demonstrate the facility of R and knowledge mining.

Assuming no past wisdom of R or information mining/statistical options, the publication covers a various set of difficulties that pose assorted demanding situations when it comes to dimension, kind of information, objectives of research, and analytical instruments. to offer the most info mining approaches and methods, the writer takes a hands-on process that makes use of a sequence of special, real-world case studies:
* Predicting algae blooms
* Predicting inventory industry returns
* Detecting fraudulent transactions
* Classifying microarray samples
With those case reviews, the writer provides all worthwhile steps, code, and data.

Web Resource
A aiding web site mirrors the do-it-yourself procedure of the textual content. It deals a suite of freely to be had R resource records that surround all of the code utilized in the case reports. the positioning additionally offers the knowledge units from the case stories in addition to an R package deal of numerous functions.

Show description

Read Online or Download Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) PDF

Similar data mining books

Advanced Methods for Knowledge Discovery from Complex Data

This publication brings jointly learn articles by way of lively practitioners and major researchers reporting contemporary advances within the box of data discovery. an outline of the sphere, taking a look at the problems and demanding situations concerned is by way of insurance of modern traits in facts mining. this gives the context for the next chapters on equipment and functions.

Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice

The phenomenon of volunteered geographic details is a part of a profound transformation in how geographic information, info, and data are produced and circulated. through situating volunteered geographic details (VGI) within the context of big-data deluge and the data-intensive inquiry, the 20 chapters during this booklet discover either the theories and functions of crowdsourcing for geographic wisdom creation with 3 sections targeting 1).

Big data Related Technologies, Challenges and Future Prospects

This Springer short offers a complete assessment of the history and up to date advancements of huge facts. the price chain of huge facts is split into 4 levels: info iteration, information acquisition, information garage and knowledge research. for every part, the e-book introduces the overall historical past, discusses technical demanding situations and studies the most recent advances.

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

Example text

925 Max. 600 a7 Min. 400 Max. 7 For instance, we can observe that there are more water samples collected in winter than in the other seasons. For numeric variables, R gives us a series of statistics like their mean, median, quartiles information and extreme values. These statistics provide a first idea of the distribution of the variable values (we return to this issue later on). In the event of a variable having some unknown values, their number is also shown following the string NAs. By observing the difference between medians and means, as well as the inter-quartile range (3rd quartile minus the 1st quartile),8 we can get an idea of the skewness of the distribution and also its spread.

The second example presents the elements of x that are both greater than 40 and less than 100. R also allows you to use a vector of integers to extract several elements from a vector. The numbers in the vector of indexes indicate the positions in the original vector to be extracted: > x[c(4, 6)] [1] -1 90 > x[1:3] [1] 0 -3 4 > y <- c(1, 4) > x[y] [1] 0 -1 15 There are also other operators, && and ||, to perform these operations. These alternatives evaluate expressions from left to right, examining only the first element of the vectors, while the single character versions work element-wise.

18 To check the existence of that function, it is sufficient to type its name at the prompt: > se Error: Object "se" not found The error printed by R indicates that we are safe to use that name. If a function (or any other object) existed with the name “se”, R would have printed its content instead of the error. 10310 If we need to execute several instructions to implement a function, like we did for the function se(), we need to have a form of telling R when the function body starts and when it ends.

Download PDF sample

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

by Anthony

Rated 4.86 of 5 – based on 41 votes