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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.


Data view of microbial indicators for prospect/non-prospect sites showing replacement of positive values with “1”.
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f3: Data view of microbial indicators for prospect/non-prospect sites showing replacement of positive values with “1”.

Mentions: Once the data is mapped in the database, all positive values of attributes are replaced by 1 for ease of association rule mining. The attribute having value “1” is representing presence of energy reserve whereas every other value reflects absence. The same is depicted in Figs 3 and 4. Seven instances of the data for each of the context variable i.e. salinity of water, temperature, humidity, rainfall and fossil pollution are collected at the time when the value of these variables was not normal.


An Algorithm of Association Rule Mining for Microbial Energy Prospection
Data view of microbial indicators for prospect/non-prospect sites showing replacement of positive values with “1”.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: Data view of microbial indicators for prospect/non-prospect sites showing replacement of positive values with “1”.
Mentions: Once the data is mapped in the database, all positive values of attributes are replaced by 1 for ease of association rule mining. The attribute having value “1” is representing presence of energy reserve whereas every other value reflects absence. The same is depicted in Figs 3 and 4. Seven instances of the data for each of the context variable i.e. salinity of water, temperature, humidity, rainfall and fossil pollution are collected at the time when the value of these variables was not normal.

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.