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Mastering R for Quantitative Finance by Edina Berlinger, Ferenc Illés, Milán Badics, Ádám Banai, Gergely Daróczi, Barbara Dömötör, Gergely Gabler, Dániel Havran, Péter Juhász, István Margitai, Balázs Márkus, Péter Medvegyev, Julia Molnár, Balázs Árpád Szűcs, Ágnes Tuza, Tamás Vadász, Kata Váradi, Ágnes Vidovics-Dancs

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K-means clustering on big data

Data frames and matrices are easy-to-use objects in R, with typical manipulations that execute quickly on datasets with a reasonable size. However, problems can arise when the user needs to handle larger data sets. In this section, we will illustrate how the bigmemory and biganalytics packages can solve the problem of too large datasets, which is impossible to handle by data frames or data tables.

Note

The latest updates of bigmemory, biganalytics, and biglm packages are not available on Windows at time of writing this chapter. The examples shown here assume that R Version 2.15.3 is the current state-of-the-art version of R for Windows.

In the following example, we will perform K-means clustering on large datasets. ...

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