Path association rule mining

10/24/2022
by   Yuya Sasaki, et al.
0

Graph association rule mining is a data mining technique used for discovering regularities in graph data. In this study, we propose a novel concept, path association rule mining, to discover the correlations of path patterns that frequently appear in a given graph. Reachability path patterns (i.e., existence of paths from a vertex to another vertex) are applied in our concept to discover diverse regularities. We show that the problem is NP-hard, and we develop an efficient algorithm in which the anti-monotonic property is used on path patterns. Subsequently, we develop approximation and parallelization techniques to efficiently and scalably discover rules. We use real-life graphs to experimentally verify the effective

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/27/2009

Towards the Patterns of Hard CSPs with Association Rule Mining

The hardness of finite domain Constraint Satisfaction Problems (CSPs) is...
research
09/17/2015

Class Association Rules Mining based Rough Set Method

This paper investigates the mining of class association rules with rough...
research
09/21/2019

Automatic Weighted Matching Rectifying Rule Discovery for Data Repairing

Data repairing is a key problem in data cleaning which aims to uncover a...
research
06/03/2023

Valid path-based graph vertex numbering

A labelling of a graph is an assignment of labels to its vertex or edge ...
research
02/02/2021

Mining Feature Relationships in Data

When faced with a new dataset, most practitioners begin by performing ex...
research
09/25/2010

Optimal Bangla Keyboard Layout using Data Mining Technique

This paper presents an optimal Bangla Keyboard Layout, which distributes...
research
12/13/2019

A Bayesian Approach to Rule Mining

In this paper, we introduce the increasing belief criterion in associati...

Please sign up or login with your details

Forgot password? Click here to reset