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

By Luis Torgo

The flexible functions and massive set of add-on programs make R a superb replacement to many latest and infrequently dear facts mining instruments. Exploring this quarter 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 information mining.

Assuming no previous wisdom of R or info mining/statistical ideas, the booklet covers a various set of difficulties that pose diverse demanding situations by way of measurement, form of info, ambitions of research, and analytical instruments. to provide the most info mining techniques and methods, the writer takes a hands-on strategy that makes use of a chain of specified, real-world case studies:
* Predicting algae blooms
* Predicting inventory marketplace returns
* Detecting fraudulent transactions
* Classifying microarray samples
With those case reviews, the writer provides all important steps, code, and data.

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

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

Example text

16 The existence of NA values in mnO2 also has some impact on the data to be used for drawing the graph. 5 we will see a better solution to this. can check the actual values of the created intervals by printing the created discretized version of the variable.

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. In this example, “i” should take the values in the set 1:10, that is, 1, 2, 3, . . , 10. This means that the two instructions inside the for are executed ten times, each time with i set to a different value.

The data that will be used to obtain the predictive models). strings=c('XXXXXXX')) The parameter header=F indicates that the file to be read does not include a first line with the variables names. ’ character to separate decimal places. These two previous parameter settings could have been omitted as we are using their default values. names allows us to provide a vector with the names to give to the variables whose values are being read. strings serves to indicate a vector of strings that are to be interpreted as unknown values.

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