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 |
| State | Published - Mar 2000 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Scalable feature mining for sequential data. / Lesh, Neal; Zaki, Mohammed J.; Ogihara, Mitsunori.
In: IEEE Intelligent Systems and Their Applications, Vol. 15, No. 2, 03.2000, p. 48-56.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Scalable feature mining for sequential data
AU - Lesh, Neal
AU - Zaki, Mohammed J.
AU - Ogihara, Mitsunori
PY - 2000/3
Y1 - 2000/3
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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UR - http://www.scopus.com/inward/citedby.url?scp=0033893162&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0033893162
VL - 15
SP - 48
EP - 56
JO - IEEE Intelligent Systems
JF - IEEE Intelligent Systems
SN - 1541-1672
IS - 2
ER -