Computer science data ification

Computer Science Computer Science Data ification models refer to the ification of data in ways, which ensure that the use of data is both efficient and effective. The classification of data is based on the value that the data accords to an organization. The most important data tends to be stored in media that are fast while the least critical data is stored in slow media. As a result of the improved modes of data classification, it becomes possible to utilize data for various purposes in an organization. One of the models of data classification is equal area model of classification. In this method, there is classification of polygon features through coming up with breakpoints. At the end, there is equal total area of all the polygons (Palumbo & Società Italiana di Statistica, 2010).
The other model of data classification is the equal interval model of data classification, where there are equal data ranges in the classes used for classification. This means the number of data classes is used as the basis for dividing the whole set of data. The result is that there is an equal interval occupied by each of classes used for classification. This method is preferred since there is ease of interpretation. The comparison between the categories used for classification is that there are equal data sets that result from the classification. In addition, data classes in the classified data tend to be equal. The difference between these two methods of data classification is that interpretation of the classified data tends to vary. In equal interval method, data interpretation is easy while data classification in equal area model, there are challenges when it comes to interpreting data (Palumbo & Società Italiana di Statistica, 2010).
References
Palumbo, F., & Società Italiana di Statistica. (2010). Data analysis and classification: Proceedings of the 6th Conference of the Classification and Data Analysis Group of the Società Italiana di Statistica. Berlin: Springer.