R ve Veri Madenciliği: Örnekler ve Vaka Çalışmaları

Study Notes

The document is a guide on using R for data mining, providing examples and case studies.
The author is Yanchang Zhao, with contact information.
The publication date is October 20, 2015.
Case studies are not included in the online version, but are part of the physical book edition.
The latest online version is available at given URLs.
R code, data, and FAQs are available at provided URLs.
Additional topics such as topic modelling, stream graph, spatial data analysis, and performance evaluation are suggested to be added.
Discussion forum and Twitter account information are available to post questions or feedback.
There's a related book titled "Data Mining Application with R".
The document contains the table of contents with chapter titles.
The document lists figures and abbreviations.

Data mining is the extraction of knowledge from large datasets.
Techniques include; classification, prediction, clustering, outlier detection, association rules, sequence analysis, time series analysis, and text mining.
The book follows the CRISP-DM methodology, focusing on the modelling phase.
R is a free software environment for statistical computing used for data analysis.
The CRAN task views guide users to appropriate R packages.
RStudio is an integrated development environment for R programming.
There are examples on datasets such as Iris and Bodyfat.
Datasets are described in detail, including the variables and their types (numeric, categorical).