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G.A. Putri Saptawati
Software Engineering & Data Research Group, School of electrical Engineering & Informatics, ITB
Computer Science & IT

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TPDA2 ALGORITHM FOR LEARNING BN STRUCTURE FROM MISSING VALUE AND OUTLIERS IN DATA MINING Sitohang, Benhard; Saptawati, G.A. Putri
Jurnal Informatika Vol 7, No 2 (2006): NOVEMBER 2006
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (256.81 KB) | DOI: 10.9744/informatika.7.2.pp. 108-113

Abstract

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.
Co-Authors Benhard Sitohang