Clustering and summarising association rules mined from phenotype, genotype and environmental data concerning age-related hearing impairment.
In this study, we examine the applicability of association rules for analysing high-dimensional data concerning age-related hearing impairment (ARHI). The ARHI data of the study contain hundreds of variables concerning phenotype, genotype and environmental factors. The number of association rules produced from the data is too large for manual exploration in the raw and furthermore, the rules are overlapping. Thus, the focus of our study is to develop an approach to cluster association rules into subsets and to summarise and represent the found rule subsets for easier exploration of rules. The results show that it is possible to efficiently extract rules representing interesting environmental factor-gene or gene-gene interactions. Finding suitable parameters for the association rule mining and the possibility to post-process the mined rules is essential. The developed approach facilitates rule exploration by grouping rules with items concerning the same phenomenon to the same subset and byrevealing overlapping rules.
School of Information Sciences, University of Tampere, Finland.
Kati
K
Iltanen
Sami
S
Kiviharju
Lida
L
Ao
Martti
M
Juhola
Ilmari
I
Pyykkö
D017677:Age Distribution; D000368:Aged; D000465:Algorithms; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods; D004777:Environment; D005060:Europe / Q000453:epidemiology; D005260:Female; D020022:Genetic Predisposition to Disease / Q000453:epidemiology* / Q000235:genetics*; D005838:Genotype; D034381:Hearing Loss / Q000453:epidemiology* / Q000235:genetics*; D006801:Humans; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D012307:Risk Factors; D017678:Sex Distribution