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Model-driven data mining engineering: from solution-driven implementations to 'composable' conceptual data mining models

Author

Listed:
  • Alfredo Cuzzocrea
  • Jose-Norberto Mazon
  • Juan Trujillo
  • Jose Zubcoff

Abstract

Data mining lacks a general modelling architecture allowing analysts to consider and interpret it as a truly software-engineering process, which would be beneficial for a wide spectrum of modern application scenarios. Bearing this in mind, in this paper, we propose an innovative model-driven engineering approach of data mining whose main goal consists in overcoming well-recognised limitations of actual approaches. The cornerstone of our proposal relies on the definition of a set of suitable model transformations which are able to automatically generate both the data under analysis, which are deployed via well-consolidated data warehousing technology and the analysis models for the target data mining tasks, which are tailored to a specific data-mining/analysis platform. These modelling tasks are now entrusted to the model-transformation scaffolds and rely on top of a well-defined reference architecture. The feasibility of our approach is finally demonstrated and validated by means of a comprehensive set of case studies.

Suggested Citation

  • Alfredo Cuzzocrea & Jose-Norberto Mazon & Juan Trujillo & Jose Zubcoff, 2011. "Model-driven data mining engineering: from solution-driven implementations to 'composable' conceptual data mining models," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 3(3), pages 217-251.
    • Handle: RePEc:ids:ijdmmm:v:3:y:2011:i:3:p:217-251
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