Data Preparation for Data Mining (The Morgan Kaufmann Series by Dorian Pyle

By Dorian Pyle

I've got loads of event getting ready facts for research. i used to be searching for a publication that will upload to my realizing of and increase my association for facts practise. this isn't that e-book. At top, the ebook offers perception into the categories of concerns confronted in getting ready facts and emphasizes the worth of such. instead of criticize, I desire to foreworn those that have already practiced at a slightly rigorous point (more than 5 semesters of statistics/data mining) that this could no longer be what you're looking.

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Extra resources for Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems)

Example text

For instance, in a paper mill, where paper is made, the key parts of the process were captured in the shift foreman’s experience. At shift change, the new foreman, who had enormous experience, would make various adjustments based on such measures as the taste of the process (actually tasting the slurry as a means of measuring what was happening in the mixture) at a particular stage. Each foreman knew how to tune the process to produce fine paper. Each foreman knew what was going wrong when indeed things were going wrong, and how to fix them.

A simple illustration of such a physical measurement is measuring a distance with a ruler. A nonphysical measurement might be of an opinion poll calibrated in percentage points of one opinion or another. There are several ways in which a measurement may be in error. It may be that the quantity is not correctly compared to the calibration. For instance, the ruler may simply slip out of position, leading to an inaccurate measurement. The calibration device may be inaccurate—for instance, a ruler that is longer or shorter than the standard length.

The next stage in the process was that the applications were decisioned. This approve/decline information was entered into the system. The system now had additional factors in its environment to control—targeting not only people who would respond, but also those most likely to be approved. Once again, this model was automatically maintained, without human intervention, by the continuously learning system. Following this, additional automatic environmental controls were added. The first was added when pattern of use information became available.

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