Data mining : a knowledge discovery approach by Krzysztof J Cios; et al

By Krzysztof J Cios; et al

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Provide details about the project that used the model, and discuss what benefits were achieved by deploying the model. (hint: see Hirji, K. 2001. Exploring data mining implementation. Communications of the ACM, 44(7), 87–93) 7. Provide a one-page summary of the PMML language standard. Your summary must include information about the newest release of the standard and which data mining models are supported by the standard. Part 2 Data Understanding 3 Data In this Chapter, we discuss attribute and data types, data storage, and problems of quantity and quality of data.

Values. In some domains (as in medicine), it is common to encounter data with a large percentage of missing values, even over 50% of all values. The methods for dealing with these missing values can be divided into two categories: – Removal of missing data. In this case the objects and/or features with missing values are simply discarded. This approach is effective only when the removed features are not crucial to the analysis, since the removal would than result in decreasing the information content of the data.

These systems addressed specific business problems, such as fraud detection, and provided an interface designed to hide the internal complexity of data mining methods. Some of the suites also introduced knowledge discovery process models to guide the user’s work. , and Clementine® , which uses the CRISP-DM process model. The KDP model was first discussed during the inaugural workshop on Knowledge Discovery in Databases in 1989 [14]. The main driving factor in defining the model was acknowledgment of the fact that knowledge is the end product of a data-driven discovery process.

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