Intelligent Decision Support System Based on Data Mining: Foreign Trading Case Study

Zhang, Fan; Yang, Bian; Song, Wei; Li, Linna

doi:10.1109/icca.2007.4376609

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…The three core areas of the ETL processes are, firstly, extraction, the activity of extracting data from various sources and collating it to a target database. Transformation, or data pre-processing is the foundation for data analysis and mining (Zhang et al, 2007). Today's real-world databases are highly susceptible to noisy, missing and inconsistent data due to their typically huge size (Han and Kamber, 2006).…”

Section: Etl In Data Miningmentioning

confidence: 99%

A comparative study of data mining techniques in predicting consumers credit card risk in banks

Sheng¹,

Wah²

2013

Afr. J. Bus. Manage.

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This paper investigates the use of batch and incremental classifiers such as logistic regression, neural networks, C5, naïve bayes updateable, IBk (instance-based learner, k nearest neighbour) and raced incremental logit boost to obtain the best classifier to be used for improving the predictive accuracy of consumers' credit card risk of a bank in Malaysia. Prior to generating all the models for comparison, the initial set of data is also loaded into an ETL (extraction, transformation, loading) system developed to perform feature selection or attribute relevancy analysis using ID3 algorithm, compiling a subset of data with the highest information gain and gain ratio. An extended test is performed to use equal length binning on some attributes to find if it affects the relevancy of each attribute. The selected subset of data of 24 months is used to generate various data mining models using different training and testing sizes and binning sizes. C5 emerged consistently as the technique that have generated the best models with an average predictive accuracy as high as 94.68%. Sample sizes, equal-length binning sizes and training and testing sizes are all shown to have an effect on accuracy in different intensity.

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Section: Etl In Data Miningmentioning

confidence: 99%

A comparative study of data mining techniques in predicting consumers credit card risk in banks

Sheng¹,

Wah²

2013

Afr. J. Bus. Manage.

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show abstract

“…Knowledge discovery is defined as "the non-trivial extraction of implicit, unknown, and potentially useful information from data" [8], [9]. Data mining or knowledge discovery in databases used tools and techniques for exploration of databases to extract relevant and interesting hidden relationships between variables [8], [10]. Different data mining techniques are applied to extract valuable and hidden information.…”

Section: A Data Warehouse Mining and Knowledge Discoverymentioning

confidence: 99%

Revised Framework for ETL Workflow Management for Efficient Business Decision-Making

Malik¹,

Shamim²,

Bibi³

et al. 2013

IJCTE

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Abstract-Business decision-making is not a simple task. There are many reasons for that but the main reason is data comes from heterogeneous operational sources of an organization. Therefore, it is difficult to organize and maintain especially if a huge volume of data is involved. A data warehouse is helpful in this regard as it can assists in business decision-making. Data collection and loading it into a data warehouse is difficult job because data sources are not in consistent form. This job usually consists of three main processes that involve extraction, transformation and loading. To extract the data from different sources, then transform it into a unified format and consequently load it into the warehouse, ETL (Extract, transform and load) tools are required. Nowadays, the majority of ETL tools organize workflow. An ETL workflow can be considered as a group of ETL jobs with dependencies between them. In this research paper a revised ETL workflow management framework which is based upon different considerations is proposed. These considerations along with the addition of the components in the workflow scheduling layer would help in making more effective and quality business decisions.

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Intelligent Decision Making Based on Data Mining Using Differential Evolution Algorithms and Framework for ETL Workflow Management

Shaikh

Malik

Qureshi

et al. 2010

2010 Second International Conference on Computer Engineering and Applications

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Intelligent Decision Support System Based on Data Mining: Foreign Trading Case Study

Abstract: In this paper, we propose an Intelligent Decision

Cited by 5 publications

References 14 publications

A comparative study of data mining techniques in predicting consumers credit card risk in banks

A comparative study of data mining techniques in predicting consumers credit card risk in banks

Revised Framework for ETL Workflow Management for Efficient Business Decision-Making

Intelligent Decision Making Based on Data Mining Using Differential Evolution Algorithms and Framework for ETL Workflow Management

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