In this chapter, we looked at text mining, which is to find brand new or unknown information by extracting information from the documents dataset. We also looked at how text summarization presents the condensed result of the document set under research; in other words, takes a source, extracts contents, and presents the key contents in a condensed format that is sensitive to the final needs. Also, we looked at how genre classification discriminates between documents by form, style, and the targeted system or audience. We also covered the question answering system, topic detection, and web mining.

In this book, many useful data-mining algorithms are illustrated in the form of the R language, which has been there for years, even decades. The most popular algorithms are included with a detailed description. You can start working with the knowledge structure of the classical and living data-mining algorithms and solutions built with R.