Toward data mining engineering: A software engineering approach

Marbán, Óscar; Segovia, Javier; Menasalvas, Ernestina; Galan, Luis Mengual

doi:10.1016/j.is.2008.04.003

Cited by 62 publications

(53 citation statements)

References 31 publications

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“…E.g. it has been identified that CRISP-DM lacks in the deployment phase [4], in guidance towards implementing particular tasks of data mining methodologies [5], and in the definition of phases important for engineering projects [6]. In addition, we detected that many redundancies and inefficiencies exists when following the CRISP standard data mining process model in parallel to standard BPM approaches [2].…”

Section: Introductionmentioning

confidence: 91%

On Reusing Data Mining in Business Processes - A Pattern-Based Approach

Wegener

Rüping

2011

Business Process Management Workshops

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Abstract. Today's business applications demand high flexibility in processing information and extracting knowledge from data. Thus, data mining becomes more and more an integral part of operating a business. However, the integration of data mining into business processes still requires a lot of coordination and manual adjustment. This paper aims at reducing this effort by reusing successful data mining solutions. We describe a novel approach on facilitating the integration based on process patterns for data mining and demonstrate that these patterns allow for easy reuse and can significantly speed up the process of integration. We empirically evaluate our approach in a case study of fraud detection in the health care domain.

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Section: Introductionmentioning

confidence: 91%

On Reusing Data Mining in Business Processes - A Pattern-Based Approach

Wegener

Rüping

2011

Business Process Management Workshops

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“…"Even if the analyst will not carry out the deployment effort it is important for the customer to understand up front what actions will need to be carried out in order to actually make use of the created models" [1]. Thus, the CRISP-DM model lacks in the deployment phase [17] and misses phases important for engineering projects [18]. There is no specification or support for standards on how to deploy the data mining results into the business.…”

Section: Related Workmentioning

confidence: 99%

“…Based on experience in software engineering, [18] proposes a model for data mining engineering that includes engineering-related phases which are missing in CRISP-DM. They identify as open issue, that available process models specify what to do, but not how to do it.…”

Section: Related Workmentioning

confidence: 99%

On Integrating Data Mining into Business Processes

Wegener

Rüping

2010

Business Information Systems

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Abstract. Integrating data mining into business processes becomes crucial for business today. Modern business process management frameworks provide great support for flexible design, deployment and management of business processes. However, integrating complex data mining services into such frameworks is not trivial due to unclear definitions of user roles and missing flexible data mining services as well as missing standards and methods for the deployment of data mining solutions. This work contributes an integrated view on the definition of user roles for business, IT and data mining and discusses the integration of data mining in business processes and its evaluation in the context of BPR.

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“…It has become very important because of an increased demand for methodologies and tools that can help the analysis and understanding of huge amounts of data generated on a daily basis by institutions. Knowledge discovery has been successfully used in various application areas: engineering [1], education [2], business and finance [3], insurance [4], telecommunication [5], chemistry [6], and medicine [7]. There are many techniques which are used for knowledge extraction from databases such as neural networks [8][9], genetic algorithms [10][11], decision tree [12][13], instance-based learning [14][15], rule induction [16][17], and support vector machine [18][19].…”

Section: Introductionmentioning

confidence: 99%

Destructive Algorithm for Rule Extraction based on a Trained Neural Network

ElAlami¹

2012

IJCA

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The present paper introduces a new destructive algorithm for rule extraction based on a trained neural network. The degree of complexity of neural network increases exponentially as a factor of the numbers of input and hidden nodes. Therefore, the dimensionality of the trained neural network is reduced by using a proposed destructive algorithm to extract only the most effective values of the input attributes which have higher impact on the output result for each class. Thus, the searching efficiency is highly increased and the computation is dramatically reduced for extracting rules. The generated rules from the proposed model are fired through two levels for each class. As for the first level, it deals with each individual effective input value, and the second level is concerned with each possible conjunction of the effective input values. Moreover, the proposed model extracts the strongest rules which represent a large number of instances from the database by adjusting the similarity measure threshold value. Finally, the proposed model is evaluated on different public-domain datasets and compared with standard learning models from WEKA, then the results assert that the set of rules extraction from the proposed method is more accurate and concise compared with those obtained by the other models.

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Toward data mining engineering: A software engineering approach

Cited by 62 publications

References 31 publications

On Reusing Data Mining in Business Processes - A Pattern-Based Approach

On Reusing Data Mining in Business Processes - A Pattern-Based Approach

On Integrating Data Mining into Business Processes

Destructive Algorithm for Rule Extraction based on a Trained Neural Network

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