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

By Luis Torgo

The flexible features and big set of add-on applications make R a very good replacement to many present and infrequently dear facts mining instruments. Exploring this quarter from the point of view of a practitioner, Data Mining with R: studying with Case Studies makes use of useful examples to demonstrate the ability of R and knowledge mining.

Assuming no earlier wisdom of R or info mining/statistical ideas, the booklet covers a various set of difficulties that pose varied demanding situations by way of measurement, form of facts, ambitions of research, and analytical instruments. to offer the most facts mining procedures and strategies, the writer takes a hands-on procedure that makes use of a chain of precise, real-world case studies:
<OL>
* Predicting algae blooms
* Predicting inventory marketplace returns
* Detecting fraudulent transactions
* Classifying microarray samples
</OL>
With those case stories, the writer provides all helpful steps, code, and data.

Web Resource
A helping site mirrors the do-it-yourself strategy of the textual content. It bargains a suite of freely to be had R resource documents that surround the entire code utilized in the case reports. the location additionally presents the information units from the case reviews in addition to an R package deal of numerous functions.

Show description

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

Similar data mining books

Machine Learning: The Art and Science of Algorithms that Make Sense of Data

As the most entire computer studying texts round, this e-book does justice to the field's wonderful richness, yet with out wasting sight of the unifying rules. Peter Flach's transparent, example-based strategy starts through discussing how a junk mail clear out works, which provides an instantaneous creation to desktop studying in motion, with no less than technical fuss.

Fuzzy logic, identification, and predictive control

The complexity and sensitivity of contemporary business tactics and structures more and more require adaptable complicated regulate protocols. those controllers must be capable of care for conditions not easy ôjudgementö instead of uncomplicated ôyes/noö, ôon/offö responses, situations the place an obscure linguistic description is frequently extra proper than a cut-and-dried numerical one.

Data Clustering in C++: An Object-Oriented Approach

Info clustering is a hugely interdisciplinary box, the target of that's to divide a suite of items into homogeneous teams such that gadgets within the similar staff are comparable and gadgets in numerous teams are fairly particular. millions of theoretical papers and a couple of books on info clustering were released over the last 50 years.

Fifty Years of Fuzzy Logic and its Applications

Entire and well timed record on fuzzy common sense and its applications
Analyzes the paradigm shift in uncertainty administration upon the creation of fuzzy logic
Edited and written by means of best scientists in either theoretical and utilized fuzzy logic

This e-book offers a complete record at the evolution of Fuzzy common sense considering that its formula in Lotfi Zadeh’s seminal paper on “fuzzy sets,” released in 1965. furthermore, it includes a stimulating sampling from the extensive box of study and improvement encouraged by means of Zadeh’s paper. The chapters, written by means of pioneers and well-liked students within the box, exhibit how fuzzy units were effectively utilized to synthetic intelligence, keep an eye on idea, inference, and reasoning. The publication additionally stories on theoretical matters; positive aspects fresh functions of Fuzzy good judgment within the fields of neural networks, clustering, facts mining and software program trying out; and highlights a huge paradigm shift brought on by Fuzzy common sense within the region of uncertainty administration. Conceived by way of the editors as an instructional occasion of the fifty years’ anniversary of the 1965 paper, this paintings is a must have for college students and researchers prepared to get an inspiring photograph of the possibilities, barriers, achievements and accomplishments of Fuzzy Logic-based systems.

Topics
Computational Intelligence
Data Mining and data Discovery
Control
Artificial Intelligence (incl. Robotics)

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

Sample text

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.

2 Finally, indexes may be empty, meaning that all elements are selected. An empty index represents the absence of a restriction on the selection process. For instance, if you want to fill in a vector with zeros, you could simply do “x[] <- 0”. Please notice that this is different from doing “x <- 0”. ) will fill in all current elements of x with zeros. Try both! 8 19 Matrices and Arrays Data elements can be stored in an object with more than one dimension. This may be useful in several situations.

For instance, if you want to fill in a vector with zeros, you could simply do “x[] <- 0”. Please notice that this is different from doing “x <- 0”. ) will fill in all current elements of x with zeros. Try both! 8 19 Matrices and Arrays Data elements can be stored in an object with more than one dimension. This may be useful in several situations. Arrays store data elements in several dimensions. Matrices are a special case of arrays with two single dimensions. Arrays and matrices in R are nothing more than vectors with a particular attribute that is the dimension.

Download PDF sample

Rated 4.72 of 5 – based on 4 votes