Sequential Data Analysis Task

A Sequential Data Analysis Task is a data analysis task that accepts an ordered dataset.

• AKA: Sequence Mining.
• Context:
  • It can be solved by a Sequential Data Analysis System (that implements a sequential data analysis algorithm).
  • It can range from being a Univariate Sequential Data Analysis Task to being a Multivariate Sequential Data Analysis Task.
• Example(s):
  • Temporal Data Analysis Task.
  • DNA Data Analysis Task.
  • a Sequence Pattern Mining Task.
  • a Sequence Classification Mining Task.
  • a Sequence Labeling Task.
  • a Forecasting Task.
  • …
• Counter-Example(s):
  • IID Mining.
• See: Sequence Labeling, IID Dataset, Semi-Structured Data Mining, Stream Mining Task.

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References

2013

• http://en.wikipedia.org/wiki/Sequence_mining
  • Sequence 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. Sequence 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.

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