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

By Luis Torgo

The flexible services and big set of add-on programs make R an exceptional substitute to many latest and sometimes dear info mining instruments. Exploring this region from the viewpoint of a practitioner, Data Mining with R: studying with Case Studies makes use of sensible examples to demonstrate the facility of R and knowledge mining.

Assuming no earlier wisdom of R or information mining/statistical recommendations, the booklet covers a various set of difficulties that pose diverse demanding situations when it comes to measurement, form of info, targets of study, and analytical instruments. to provide the most information mining methods and strategies, the writer takes a hands-on method that makes use of a sequence of targeted, real-world case studies:
* Predicting algae blooms
* Predicting inventory industry returns
* Detecting fraudulent transactions
* Classifying microarray samples
With those case reports, the writer provides all useful steps, code, and data.

Web Resource
A aiding site mirrors the do-it-yourself strategy of the textual content. It deals a suite of freely on hand R resource records that surround all of the code utilized in the case reviews. the positioning additionally offers the knowledge units from the case experiences in addition to an R package deal of a number of functions.

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

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The following function illustrates this and also the use of parameters with default values, 18 You do not have to worry about overriding the definition of the R function. It will continue to exist, although your new function with the same name will be on top of the search path of R, thus “hiding” the other standard function. x) - 3 + } + unlist(stats) + } This function has a parameter (more) that has a default value (F). This means that you can call the function with or without setting this parameter.

There are several types of index vectors. Logical index vectors extract the elements corresponding to true values. Let us see a concrete example: > x <- c(0, -3, 4, -1, 45, 90, -5) > x > 0 [1] FALSE FALSE TRUE FALSE TRUE TRUE FALSE Introduction 17 The second instruction of the code shown above is a logical condition. ), thus producing a vector with as many logical values as there are elements in x. If we use this vector of logical values to index x, we get as a result the positions of x that correspond to the true values: > x[x > 0] [1] 4 45 90 This reads as follows: Give me the positions of x for which the following logical expression is true.

In the case of this function, the two instructions that calculate the kurtosis and skewness of the vector of values are only executed if the variable more is true; otherwise they are skipped. Another important instruction is the for(). This instruction allows us to repeat a set of commands several times. g. f(5)). The instruction for in this function says to R that the instructions “inside of it” (delimited by the curly braces) are to be executed several times. Namely, they should be executed with the variable “i” taking different values at each repetition.

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