Extracting co-occurrence relations from ZDDs
type: Item
instance of: scholarly article
title: Extracting co-occurrence relations from ZDDs
zbMATH Open document ID: 1461.68252
author: Takahisa Toda
published in: Algorithms
publication date: 2019-03-26T00:00:00Z
review text: Summary: A zero-suppressed binary decision diagram (ZDD) is a graph representation suitable for handling sparse set families. Given a ZDD representing a set family, we present an efficient algorithm to discover a hidden structure, called a co-occurrence relation, on the ground set. This computation can be done in time complexity that is related not to the number of sets, but to some feature values of the ZDD. We furthermore introduce a conditional co-occurrence relation and present an extraction algorithm, which enables us to discover further structural information.
Mathematics Subject Classification ID: 68W05
Mathematics Subject Classification ID: 68P05
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7042146
zbMATH Keywords: BDD
zbMATH Keywords: ZDD
zbMATH Keywords: partition
zbMATH Keywords: co-occurrence
zbMATH Keywords: data mining
describes a project that uses: GraphBase
describes a project that uses: CUDD
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a5040654
OpenAlex ID: W2078252815
cites work: Binary Decision Diagrams
cites work: Graph-Based Algorithms for Boolean Function Manipulation
cites work: Partially-shared zero-suppressed multi-terminal BDDs: Concept, algorithms and applications
cites work: Q2957449
cites work: Q3140399
DOI: 10.3390/A5040654
description: scientific article; zbMATH DE number 7042146