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An Algorithm of Association Rule Mining for Microbial Energy Prospection

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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.


Process of context based association rule mining for microbial energy prospection.
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f1: Process of context based association rule mining for microbial energy prospection.

Mentions: An application of CBPNARM on microbial databases is presented as first application of the algorithm for microbial energy prospection. In this study, microbial data is collected from different sources and stored in a relational database. Apriori algorithm is applied to extract frequent item sets from the database. Three parameters of association rules are defined then, i.e. support, confidence and interestingness. The parameters are defined in section 3 and 4. On the basis of support and confidence, frequent item sets are extracted from the database. These rules also contain some rules which are not of potential interest. Such rules are eliminated on the basis of interestingness measure. Pruning of uninteresting association rules is done once in the above stated method. In the proposed technique, the rules are re oriented on the basis of context variable and pruned again on the basis of interestingness measure. For microbial data, salinity of water,temperature, humidity, rainfall and fossil pollution are considered as context variables. All the context variables are stored with CIV and CFV. The values of variables are re-adjusted according to the method given in Table 3. The readjustment of values would cause to generate a database with new values of microbial indicators and new set of association rules. The proposed method is given in detail in Figs 1 and 2. The microbial database of our study contains two tables. The structure of the tables is given in Table 4 and Table 5.


An Algorithm of Association Rule Mining for Microbial Energy Prospection
Process of context based association rule mining for microbial energy prospection.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: Process of context based association rule mining for microbial energy prospection.
Mentions: An application of CBPNARM on microbial databases is presented as first application of the algorithm for microbial energy prospection. In this study, microbial data is collected from different sources and stored in a relational database. Apriori algorithm is applied to extract frequent item sets from the database. Three parameters of association rules are defined then, i.e. support, confidence and interestingness. The parameters are defined in section 3 and 4. On the basis of support and confidence, frequent item sets are extracted from the database. These rules also contain some rules which are not of potential interest. Such rules are eliminated on the basis of interestingness measure. Pruning of uninteresting association rules is done once in the above stated method. In the proposed technique, the rules are re oriented on the basis of context variable and pruned again on the basis of interestingness measure. For microbial data, salinity of water,temperature, humidity, rainfall and fossil pollution are considered as context variables. All the context variables are stored with CIV and CFV. The values of variables are re-adjusted according to the method given in Table 3. The readjustment of values would cause to generate a database with new values of microbial indicators and new set of association rules. The proposed method is given in detail in Figs 1 and 2. The microbial database of our study contains two tables. The structure of the tables is given in Table 4 and Table 5.

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.