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Author: Admin | 2025-04-28
The user, new data can be added to the data set or mining can be further refined or a different data mining method can be chosen to get more accurate results. Thus, KDD is completely an iterative process.When we analyze different steps of KDD process, we could understand that we are mining data to get useful information or knowledge. Thus, knowledge mining would be the more appropriate term rather than data mining.KNOWLEDGE DISCOVERY PROCESSMODELSAlthough the models usually emphasize independence from specific applications and tools, theycan be broadly divided into those that take into account industrial issues and those that do not.However, the academic models, which usually are not concerned with industrial issues, can bemade applicable relatively easily in the industrial setting and vice versa. We restrict our discussion to those models that have been popularized in the literature and have been used in real knowledgediscovery projects.ACADEMIC RESEARCH MODELSThe efforts to establish a KDP model were initiated in academia. In the mid-1990s, when the DMfield was being shaped, researchers started defining multistep procedures to guide users of DMtools in the complex knowledge discovery world. The main emphasis was to provide a sequenceof activities that would help to execute a KDP in an arbitrary domain. The two process modelsdeveloped in 1996 and 1998 are the nine-step model by Fayyad et al. and the eight-step modelby Anand and Buchner. Below we introduce the first of these, which is perceived as the leadingresearch model. The second model is summarizedKNOWLEDGE DISCOVERY PROCESSMODELSThe Fayyad et al. KDP model consists of nine steps, which are outlined as follows:Developing and understanding the application domain. This step includes learning the relevantprior knowledge and the goals of the end user of the discovered knowledge.Creating a target data set. Here the data miner selects a subset of variables (attributes) anddata points
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