Presentation 2007-05-31
Recent Topics on Data Mining and Knowledge Discovery Using Binary Decision Diagrams
Shin-ichi MINATO,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) Binary Decision Diagrams (BDDs) are the efficient data structure for representing. Boolean functions on the main memory. The techniques of BDD manipulation have been developed in the area of VLSI logic design since 1990's. Recently, we found that the BDD-based techniques can also be applied effectively to the problems of data mining and knowledge. discovery. Especially, Zero-suppressed BDDs, a type of BDDs, are suitable for handling sets of sparse combinations that often appear in the real-life database analysis. In this paper, we show the resents results including the ZBDD-based techniques for frequent itemset mining, various query processing for itemsets, and fast algorithms for extracting hidden structural information from itemsets based on ZBDD representation.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Binary Decision Diagram / BDD / Zero-suppressed BDD / ZBDD / Data mining / Knowledge discovery / Frequent itemset
Paper # AI2007-6
Date of Issue

Conference Information
Committee AI
Conference Date 2007/5/24(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Vice Chair

Paper Information
Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Recent Topics on Data Mining and Knowledge Discovery Using Binary Decision Diagrams
Sub Title (in English)
Keyword(1) Binary Decision Diagram
Keyword(2) BDD
Keyword(3) Zero-suppressed BDD
Keyword(4) ZBDD
Keyword(5) Data mining
Keyword(6) Knowledge discovery
Keyword(7) Frequent itemset
1st Author's Name Shin-ichi MINATO
1st Author's Affiliation Graduate School of Information Science and Technology, Hokkaido University()
Date 2007-05-31
Paper # AI2007-6
Volume (vol) vol.107
Number (no) 78
Page pp.pp.-
#Pages 6
Date of Issue