Learning Data Mining with R
Being able to deal with the array of problems that you may encounter during complex statistical projects can be difficult. If you have only a basic knowledge of R, this book will provide you with the skills and knowledge to successfully create and customize the most popular data mining algorithms to overcome these difficulties.
You will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. Discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on RHadoop projects. You will finish this book feeling confident in your ability to know which data mining algorithm to apply in any situation.
|Course Length||9 hours 25 minutes|
|Date Of Publication||31 Jan 2015|
|Credit card fraud detection and statistical methods|
|Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods|
|Intrusion detection and density-based methods|
|Intrusion detection and clustering-based methods|
|Monitoring the performance of the web server and classification-based methods|
|Detecting novelty in text, topic detection, and mining contextual outliers|
|Collective outliers on spatial data|
|Outlier detection in high-dimensional data|
|Time for action|