O'Reilly logo

R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Chapter 8. Mining Stream, Time-series, and Sequence Data

In this chapter, you will learn how to write mining codes for stream data, time-series data, and sequence data.

The characteristics of stream, time-series, and sequence data are unique, that is, large and endless. It is too large to get an exact result; this means an approximate result will be achieved. The classic data-mining algorithm should be extended, or a new algorithm needs to be designed for this type of the dataset.

In relation to the mining of stream, time-series, and sequence data, there are some topics we can't avoid. They are association, frequent pattern, classification and clustering algorithms, and so on. In the following sections, we will go through these major topics.

In this ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required