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TPDA2 ALGORITHM FOR LEARNING BN STRUCTURE FROM MISSING VALUE AND OUTLIERS IN DATA MINING
Jurnal Informatika
View Archive Info| Field | Value | |
| Title |
TPDA2 ALGORITHM FOR LEARNING BN STRUCTURE FROM MISSING VALUE AND OUTLIERS IN DATA MINING
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| Creator |
Sitohang, Benhard
Saptawati, G.A. Putri |
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| Subject |
missing value, noisy data, BN structure, TPDA.
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| Description |
Three-Phase Dependency Analysis (TPDA) algorithm was proved as most efficient algorithm (which requires at most O(N4) Conditional Independence (CI) tests). By integrating TPDA with "node topological sort algorithm", it can be used to learn Bayesian Network (BN) structure from missing value (named as TPDA1 algorithm). And then, outlier can be reduced by applying an "outlier detection & removal algorithm" as pre-processing for TPDA1. TPDA2 algorithm proposed consists of those ideas, outlier detection & removal, TPDA, and node topological sort node.
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| Publisher |
Institute of Research and Community Outreach - Petra Christian University
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| Contributor |
—
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| Date |
2007-02-01
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| Type |
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion — |
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| Format |
application/pdf
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| Identifier |
http://jurnalinformatika.petra.ac.id/index.php/inf/article/view/16561
10.9744/informatika.7.2.pp. 108-113 |
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| Source |
Jurnal Informatika; Vol 7, No 2 (2006): NOVEMBER 2006; pp. 108-113
2528-5823 1411-0105 |
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| Language |
eng
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| Relation |
http://jurnalinformatika.petra.ac.id/index.php/inf/article/view/16561/16553
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