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 ...
Get R: Data Analysis and Visualization now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
