Scalable feature mining for sequential data

Neal Lesh; Mohammed J. Zaki; Mitsunori Ogihara

doi:10.1109/5254.850827

Scalable feature mining for sequential data

Neal Lesh, Mohammed J. Zaki, Mitsunori Ogihara

Research output: Contribution to journal › Article › peer-review

42 Scopus citations

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 language	English (US)
Pages (from-to)	48-56
Number of pages	9
Journal	IEEE Intelligent Systems and Their Applications
Volume	15
Issue number	2
DOIs	https://doi.org/10.1109/5254.850827
State	Published - 2000
Externally published	Yes

ASJC Scopus subject areas

Engineering(all)

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title = "Scalable feature mining for sequential data",

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.",

author = "Neal Lesh and Zaki, {Mohammed J.} and Mitsunori Ogihara",

note = "Funding Information: Mitsunori Ogihara is supported in part by National Science Foundation grants CCR-9701911, CCR-9725021, and INT-9726724 and by DARPA grant F30602-98-2-0133.",

year = "2000",

doi = "10.1109/5254.850827",

language = "English (US)",

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AU - Zaki, Mohammed J.

AU - Ogihara, Mitsunori

N1 - Funding Information: Mitsunori Ogihara is supported in part by National Science Foundation grants CCR-9701911, CCR-9725021, and INT-9726724 and by DARPA grant F30602-98-2-0133.

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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.

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