Frequent Subsequence Mining Task

AKA: Sequential Pattern Mining, String Data Mining.
Context:
- It can range from being a Temporal Sequence Pattern Mining Task to being a Non-Temporal Sequence Pattern Mining Task.
- It can be solved by a Frequent Subsequence Mining System (that implements a Frequent Subsequence Mining Algorithm).
- It can range from being a Frequent Symbolic Subsequence Mining Task to being a Frequent Numeric Subsequence Mining Task.
- …
Example(s):
- a Data Stream Mining Task.
- a Frequent Substring Mining (from symbolic string datasets)
  - a Frequent Text-Substring Mining Task.
  - a Frequent DNA-Substring Mining Task.
- …
Counter-Example(s):
- IID Pattern Mining.
- Frequent Subgraph Mining.
See: Temporal Data, Frequent-Pattern Mining Algorithm, Structured Data Mining.

References

(Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Sequential_Pattern_Mining Retrieved:2015-2-8.
- Sequential Pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence. It is usually presumed that the values are discrete, and thus time series mining is closely related, but usually considered a different activity. Sequential pattern mining is a special case of structured data mining.
  There are several key traditional computational problems addressed within this field. These include building efficient databases and indexes for sequence information, extracting the frequently occurring patterns, comparing sequences for similarity, and recovering missing sequence members. In general, sequence mining problems can be classified as string mining which is typically based on string processing algorithms and itemset mining which is typically based on association rule learning.