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Review of Data mining tools

          

Reviews historical and current data mining and related tools, and proposes criteria for the tool categorization based on different user groups, data structures, data mining tasks and methods, visualization and interaction styles, import and export options for data and models, platforms, and license policies.

Wiley DMKD

Wiley Interdisciplinary Reviews:
Data Mining and Knowledge Discovery,
by Ralf Mikut, Markus Reischl, 9 MAR 2011

DOI: 10.1002/widm.24

Abstract.
The development and application of data mining algorithms requires the use of powerful software tools. As the number of available tools continues to grow, the choice of the most suitable tool becomes increasingly difficult. This paper attempts to support the decision-making process by discussing the historical development and presenting a range of existing state-of-the-art data mining and related tools.

Furthermore, we propose criteria for the tool categorization based on different user groups, data structures, data mining tasks and methods, visualization and interaction styles, import and export options for data and models, platforms, and license policies. These criteria are then used to classify data mining tools into nine different types. The typical characteristics of these types are explained and a selection of the most important tools is categorized. This paper is organized as follows: the first section Historical Development and State-of-the-Art highlights the historical development of data mining software until present; the criteria to compare data mining software are explained in the second section Criteria for Comparing Data Mining Software. The last section Categorization of Data Mining Software into Different Types proposes a categorization of data mining software and introduces typical software tools for the different types.

(c) 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 00 1-13 DOI: 10.1002/widm.24

onlinelibrary.wiley.com/doi/10.1002/widm.24/full


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