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SEMMA mainly focuses on the modeling tasks of data mining projects, leaving the business aspects out (unlike, e.g., CRISP-DM and its
Business Understanding phase). Additionally, SEMMA is designed to help the users of the SAS Enterprise Miner software. Therefore, applying it outside Enterprise Miner
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may be ambiguous. However, in order to complete the "Sampling" phase of SEMMA a deep understanding of the business aspects would have to be a requirement in order to do effective sampling. So, in effect, a business understanding would be required to effectively complete sampling.
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applications. Although SEMMA is often considered to be a general data mining methodology, SAS claims that it is "rather a logical organization of the functional tool set of" one of their products, SAS Enterprise Miner, "for carrying out the core tasks of data mining".
100:, e.g., selecting the data set for modeling. The data set should be large enough to contain sufficient information to retrieve, yet small enough to be used efficiently. This phase also deals with data partitioning.
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122:. In the Model phase the focus is on applying various modeling (data mining) techniques on the prepared variables in order to create models that possibly provide the desired outcome.
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for the diversified and iterative process of data mining that users can apply to their data mining projects regardless of industry. While the
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initiative, aimed to create a neutral methodology, SAS also offered a pattern to follow in its data mining tools.
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In the expanding field of data mining, there has been a call for a standard methodology or a simple list of
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170:. In Proceedings of the IADIS European Conference on Data Mining 2008, pp 182-185.
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A Proposed Data Mining
Methodology and its Application to Industrial Procedures
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KDD, SEMMA AND CRISP-DM: A PARALLEL OVERVIEW, Ana
Azevedo and M.F. Santos
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European
Strategic Program on Research in Information Technology
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The phases of SEMMA and related tasks are the following:
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