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Enabling Non-expert Users to Apply Data Mining for Bridging the Big Data Divide

Abstract : Non-expert users find complex to gain richer insights into the increasingly amount of available heterogeneous data, the so called big data. Advanced data analysis techniques, such as data mining, are difficult to apply due to the fact that (i) a great number of data mining algorithms can be applied to solve the same problem, and (ii) correctly applying data mining techniques always requires dealing with the inherent features of the data source. Therefore, we are attending a novel scenario in which non-experts are unable to take advantage of big data, while data mining experts do: the big data divide. In order to bridge this gap, we propose an approach to offer non-expert miners a tool that just by uploading their data sets, return them the more accurate mining pattern without dealing with algorithms or settings, thanks to the use of a data mining algorithm recommender. We also incorporate a previous task to help non-expert users to specify data mining requirements and a later task in which users are guided in interpreting data mining results. Furthermore, we experimentally test the feasibility of our approach, in particular, the method to build recommenders in an educational context, where instructors of e-learning courses are non-expert data miners who need to discover how their courses are used in order to make informed decisions to improve them.
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Contributor : Hal Ifip <>
Submitted on : Thursday, March 29, 2018 - 10:36:16 AM
Last modification on : Saturday, November 17, 2018 - 1:44:05 PM
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  • HAL Id : hal-01746410, version 1


Roberto Espinosa, Diego García-Saiz, Marta Zorrilla, Jose Zubcoff, Jose-Norberto Mazón. Enabling Non-expert Users to Apply Data Mining for Bridging the Big Data Divide. 3rd International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Aug 2013, Riva del Garda, Italy. pp.65-86. ⟨hal-01746410⟩



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