Paper Info
Title
Discovering useful patterns from multiple instance data.
Abstract
Association rule mining is one of the most common data mining techniques used to identify and describe interesting relationships between patterns from large datasets, the frequency of an association being defined as the number of transactions that it satisfies. In situations where each transaction includes an undetermined number of instances (customers shopping habits where each transaction represents a different customer having a varied number of instances), the problem cannot be described as a traditional association rule mining problem. The aim of this work is to discover robust and useful patterns from multiple instance datasets, that is, datasets where each transaction may include an undetermined number of instances. We propose a new problem formulation in the data mining framework: multiple-instance association rule mining. The problem definition, an algorithm to tackle the problem, the application fields, and the relations' quality measures are formally described. Experimental results reveal the scalability of the problem on different data dimensionality. Finally, we apply it to two real-world applications field: (1) analysis of financial data gathered from one of the most important banks in Lithuania; (2) study of existing relations between records of unemployed gathered from the Spanish public employment service.
Year
DOI
Venue
2016
10.1016/j.ins.2016.04.007
Inf. Sci.
Keywords
Field
DocType
Association rules,Multiple-instance data,Data mining
Public employment service,Data mining,Data stream mining,Computer science,Curse of dimensionality,Association rule learning,Artificial intelligence,Database transaction,Machine learning,Scalability
Journal
Volume
Issue
ISSN
357
C
0020-0255
Citations 
PageRank 
References 
6
0.52
21
Authors
4
Name
Order
Citations
PageRank
José M. Luna136623.59
Alberto Cano213011.20
Virgilijus Sakalauskas35714.64
S. Ventura42318158.44