This information is then used to make a rational decision. In this approach the human decision maker builds alternative models and defines the preference ordering criteria. On the other hand, a complementary approach to such problem solving that does not rely on collecting observational data is decision making. The most informative attributes that influenced the accuracy of data mining are computed prior or during the process of data mining. Experts are involved in most stages of a data mining project described by the CRISP-DM. However, data mining’s performance and result accuracy highly dependent on the format and the availability of data presented and also the computational data mining tools. Data cleaning and pre-processing involve the creation of the relevant data subset through data selection, as well as finding of useful properties/features, generating new features, defining appropriate feature values and/or value discretization. ![]() The third and one of the most important stages in data mining process is the data cleaning and preparation stage. There are six stages of data mining processes business understanding, data understanding, data preparation, modelling, evaluation and deployment.
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