Scalable feature mining for sequential data

Neal Lesh, Mohammed J. Zaki, Mitsunori Ogihara

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

To provide good feature selection for sequential domains, FeatureMine was developed. This scalable feature-mining algorithm combines sequence mining and classification algorithms. Tests on three practical domains demonstrate the capability to efficiently handle very large data sets with thousands of items and millions of records.

Original languageEnglish
Pages (from-to)48-56
Number of pages9
JournalIEEE Intelligent Systems and Their Applications
Volume15
Issue number2
Publication statusPublished - 1 Mar 2000
Externally publishedYes

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Feature extraction

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Lesh, N., Zaki, M. J., & Ogihara, M. (2000). Scalable feature mining for sequential data. IEEE Intelligent Systems and Their Applications, 15(2), 48-56.

Scalable feature mining for sequential data. / Lesh, Neal; Zaki, Mohammed J.; Ogihara, Mitsunori.

In: IEEE Intelligent Systems and Their Applications, Vol. 15, No. 2, 01.03.2000, p. 48-56.

Research output: Contribution to journalArticle

Lesh, N, Zaki, MJ & Ogihara, M 2000, 'Scalable feature mining for sequential data', IEEE Intelligent Systems and Their Applications, vol. 15, no. 2, pp. 48-56.
Lesh, Neal ; Zaki, Mohammed J. ; Ogihara, Mitsunori. / Scalable feature mining for sequential data. In: IEEE Intelligent Systems and Their Applications. 2000 ; Vol. 15, No. 2. pp. 48-56.
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