TPDA2 Algorithm for Learning BN Structure From Missing Value and Outliers in Data Mining  

Oleh:

Sitohang, Benhard ; Saptawati, G. A. Putri

Jenis: Article from Journal - ilmiah nasional - tidak terakreditasi DIKTI
Dalam koleksi: Jurnal Informatika vol. 7 no. 2 (Nov. 2006), page 108-113.
Topik: data mining; missing value; noisy data; BN structure; TPDA
Fulltext: benhard sitohang.pdf (250.79KB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: JJ103.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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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