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An Algorithm of Association Rule Mining for Microbial Energy Prospection

View Article: PubMed Central - PubMed

ABSTRACT

The presence of hydrocarbons beneath earth’s surface produces some microbiological anomalies in soils and sediments. The detection of such microbial populations involves pure bio chemical processes which are specialized, expensive and time consuming. This paper proposes a new algorithm of context based association rule mining on non spatial data. The algorithm is a modified form of already developed algorithm which was for spatial database only. The algorithm is applied to mine context based association rules on microbial database to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve. The surface and soil manifestations caused by the presence of hydrocarbon oxidizing microbes are selected from existing literature and stored in a shared database. The algorithm is applied on the said database to generate direct and indirect associations among the stored microbial indicators. These associations are thencorrelated with the probability of hydrocarbon’s existence. The numerical evaluation shows better accuracy for non-spatial data as compared to conventional algorithms at generating reliable and robust rules.

No MeSH data available.


Plot; Number of rules with minimum support of Apriori, PNARM and CBPNARM.
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f5: Plot; Number of rules with minimum support of Apriori, PNARM and CBPNARM.

Mentions: The resulting association rules are organized in the database in such a form that each antecedent is mapped against all of its consequents up till 7-consequent. The association rules resulted from above scenario are shown in Fig. 5. Both the positive and negative association rules are mined by considering all the assumptions. The assumptions include presence of all context variables, absence of all context variables and absence of few. The positive rules contain positive antecedent and consequent while negative association rules may contain one or both of antecedents and consequents as negative. The figure indicates clear reduction in number of association rules when the context variable is considered abnormal. From the figure it can also be observed that an increase in number of context variables do not prune large number of association rules from existing rule set.


An Algorithm of Association Rule Mining for Microbial Energy Prospection
Plot; Number of rules with minimum support of Apriori, PNARM and CBPNARM.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5385557&req=5

f5: Plot; Number of rules with minimum support of Apriori, PNARM and CBPNARM.
Mentions: The resulting association rules are organized in the database in such a form that each antecedent is mapped against all of its consequents up till 7-consequent. The association rules resulted from above scenario are shown in Fig. 5. Both the positive and negative association rules are mined by considering all the assumptions. The assumptions include presence of all context variables, absence of all context variables and absence of few. The positive rules contain positive antecedent and consequent while negative association rules may contain one or both of antecedents and consequents as negative. The figure indicates clear reduction in number of association rules when the context variable is considered abnormal. From the figure it can also be observed that an increase in number of context variables do not prune large number of association rules from existing rule set.

View Article: PubMed Central - PubMed

ABSTRACT

The presence of hydrocarbons beneath earth’s surface produces some microbiological anomalies in soils and sediments. The detection of such microbial populations involves pure bio chemical processes which are specialized, expensive and time consuming. This paper proposes a new algorithm of context based association rule mining on non spatial data. The algorithm is a modified form of already developed algorithm which was for spatial database only. The algorithm is applied to mine context based association rules on microbial database to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve. The surface and soil manifestations caused by the presence of hydrocarbon oxidizing microbes are selected from existing literature and stored in a shared database. The algorithm is applied on the said database to generate direct and indirect associations among the stored microbial indicators. These associations are thencorrelated with the probability of hydrocarbon’s existence. The numerical evaluation shows better accuracy for non-spatial data as compared to conventional algorithms at generating reliable and robust rules.

No MeSH data available.