By Yanchang Zhao
This e-book courses R clients into info mining and is helping facts miners who use R of their paintings. It presents a how-to approach utilizing R for information mining functions from academia to undefined. It
- Presents an advent into utilizing R for facts mining purposes, overlaying hottest information mining techniques
- Provides code examples and information in order that readers can simply examine the techniques
- Features case reports in real-world purposes to assist readers follow the suggestions of their paintings and studies
The R code and information for the ebook are supplied on the RDataMining.com website.
The e-book is helping researchers within the box of information mining, postgraduate scholars who're attracted to information mining, and information miners and analysts from undefined. For the various universities that experience classes on information mining, this e-book is a useful reference for college kids learning info mining and its comparable matters. moreover, it's a resource for someone excited by commercial education classes on information mining and analytics. The innovations during this e-book support readers as R turns into more and more well known for facts mining functions.
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Additional info for R and Data Mining. Examples and Case Studies
Example text
At first, it shows an example on building a linear regression model to predict CPI data. After that, it introduces logistic regression. The generalized linear model (GLM) is then presented, followed by a brief introduction of non-linear regression. 1 Linear Regression Linear regression is to predict response with a linear function of predictors as follows: y = c0 + c1 x1 + c2 x2 + · · · + ck xk , where x1 , x2 , · · · , xk are predictors and y is the response to predict. 2 At first, the data is created and plotted.
3). 3 Decision tree with package rpart. 4). 4 Selected decision tree. 73 n=7 36 R and Data Mining After that, the selected tree is used to make prediction and the predicted values are compared with actual labels. In the code below, function abline() draws a diagonal line. 5). 5 Prediction result. 1 for details of the data). There are two limitations with function randomForest(). First, it cannot handle data with missing values, and users have to impute data before feeding them into the function.
For example, banks assess the risk of home-loan applicants based on their age, income, expenses, occupation, number of dependents, total credit limit, etc. This chapter introduces basic concepts and presents examples of various regression techniques. At first, it shows an example on building a linear regression model to predict CPI data. After that, it introduces logistic regression. The generalized linear model (GLM) is then presented, followed by a brief introduction of non-linear regression.