Frequent Subsequence Mining Task

A Frequent Subsequence Mining Task is a frequent pattern mining task that is a sequence data mining task (that needs to identify frequent subsequence patterns in sequence data).

• 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.

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References

2015

• (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.

2007

• (Han et al., 2007) ⇒ Jiawei Han, Hong Cheng, Dong Xin, and Xifeng Yan. (2007). “Frequent Pattern Mining: current status and future directions.” In: Data Mining and Knowledge Discovery, 15(1). doi:10.1007/s10618-006-0059-1
  • QUOTE: Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications.

2001

• (Zaki, 2001) ⇒ Mohammed J. Zaki. (2001). “SPADE: An Efficient Algorithm for Mining Frequent Sequences.” In: Machine Learning Journal, 42(1-2). doi:10.1023/A:1007652502315
  • QUOTE: In this paper we present SPADE, a new algorithm for fast discovery of Sequential Patterns. The existing solutions to |this problem make repeated database scans, and use complex hash structures which have poor locality.

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