The Use of Association-Rule Mining and High-Dimensional Visualization To Explore the Impact of Geological Features on Dynamic-Flow Behavior

SPE Journal ◽  
2016 ◽  
Vol 21 (06) ◽  
pp. 1996-2009 ◽  
Author(s):  
Satomi Suzuki ◽  
Dave Stern ◽  
Tom Manzocchi
Keyword(s):  
Reservoir Simulation ◽  
Association Rule ◽  
Association Rule Mining ◽  
High Dimensional ◽  
Rule Mining ◽  
Geological Uncertainty ◽  
Flow Response ◽  
Geological Features ◽  
Simulation Results ◽  
The Impact

Summary Because of computational advances in reservoir simulation with high-performance computing, it is now possible to simulate more than thousands of reservoir-simulation cases in a practical time frame. This opens a new avenue to reservoir-simulation studies, enabling exhaustive exploration of subsurface uncertainty and development/depletion options. However, analyzing the results of a large number of simulation cases still remains a challenging and overwhelming task. We propose a new method that enables the efficient analysis of massive reservoir-simulation results, often consisting of thousands of cases, by discovering interesting patterns of relationships among variables in large data sets. The method uses a well-known data-mining method, called association-rule mining, together with a high-dimensional visualization technique. We demonstrate the capability of the proposed method by using it to analyze the reservoir-simulation results from the Sensitivity Analysis of the Impact of Geological Uncertainty on Production (SAIGUP) project, which is an interdisciplinary reservoir-modeling project carried out earlier by Manzocchi et al. (2008a). To investigate the influence of geological features on oil recovery in shallow marine reservoirs, numerous reservoir models were built and flow-simulated in the SAIGUP project. In this paper, we analyze the simulation results from an ensemble of 9,072 models, which cover all possible combinations of several structural and sedimentological parameters individually varied to describe geological uncertainty. To be able to analyze the simulation results from such exhaustive sampling from high-dimensional model parameter space, it is crucial to efficiently decompose complex interactions between model parameters and to discover hidden impacts on flow response. By coupling the association-rule mining algorithm and high-dimensional visualization, such interactions and impacts are rapidly extracted and visualized in such a way that engineers and geoscientists can interpret meaningful sensitivities “at a glance.” This methodology provides a novel way to rapidly interpret flow response from a large ensemble of reservoir models without being overwhelmed by massive data.

scholarly journals Association rule mining algorithms on high-dimensional datasets

2018 ◽  
Vol 23 (3) ◽  
pp. 420-427 ◽  
Author(s):  
Dongmei Ai ◽  
Hongfei Pan ◽  
Xiaoxin Li ◽  
Yingxin Gao ◽  
Di He
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
High Dimensional ◽  
Rule Mining ◽  
Mining Algorithms ◽  
High Dimensional Datasets

Inventory Classification Using Multi-Level Association Rule Mining

2019 ◽  
Vol 11 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Levels Of Abstraction ◽  
Frequent Items ◽  
Multi Level ◽  
Multiple Levels ◽  
Real Scenario ◽  
The Impact ◽  
Different Levels

Popular data mining methods support knowledge discovery from patterns that hold in relations. For many applications, it is difficult to find strong associations among data items at low or primitive levels of abstraction. Mining association rules at multiple levels may lead to more informative and refined knowledge from data. Multi-level association rule mining is a variation of association rule mining for finding relationships between items at each level by applying different thresholds at different levels. In this study, an inventory classification policy is provided. At each level, the loss profit of frequent items is determined. The obtained loss profit is used to rank frequent items at each level with respect to their category, content and brand. This helps inventory manager to determine the most profitable item with respect to their category, content and brand. An example is illustrated to validate the results. Further, to comprehend the impact of above approach in the real scenario, experiments are conducted on the exiting dataset.

Balanced Approach for Hiding Sensitive Association Rules in Data Sharing Environment

2014 ◽  
Vol 8 (3) ◽  
pp. 39-62 ◽  
Author(s):  
Janakiramaiah Bonam ◽  
Ramamohan Reddy
Keyword(s):  
Side Effects ◽  
Impact Factor ◽  
Performance Measures ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Balanced Approach ◽  
The Impact

Privacy preserving association rule mining protects the sensitive association rules specified by the owner of the data by sanitizing the original database so that the sensitive rules are hidden. In this paper, the authors study a problem of hiding sensitive association rules by carefully modifying the transactions in the database. The algorithm BHPSP calculates the impact factor of items in the sensitive association rules. Then it selects a rule which contains an item with minimum impact factor. The algorithm alters the transactions of the database to hide the sensitive association rule by reducing the loss of other non-sensitive association rules. The quality of a database can be well maintained by greedily selecting the alterations in the database with negligible side effects. The BHPSP algorithm is experimentally compared with a HCSRIL algorithm with respect to the performance measures misses cost and difference between original and sanitized databases. Experimental results are also mentioned demonstrating the effectiveness of the proposed approach.

scholarly journals Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets

Algorithms ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 21
Author(s):  
Consolata Gakii ◽  
Paul O. Mireji ◽  
Richard Rimiru
Keyword(s):  
Feature Selection ◽  
Association Rule ◽  
Association Rule Mining ◽  
Principal Component ◽  
Recursive Feature Elimination ◽  
High Dimensional ◽  
Rule Mining ◽  
Rnaseq Data ◽  
Feature Selection Approach ◽  
Feature Selection Techniques

Analysis of high-dimensional data, with more features () than observations () (), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.

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