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 trying to find a booklet that will upload to my knowing of and improve my association for info instruction. this isn't that booklet. At top, the booklet presents perception into the categories of matters 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 would no longer be what you're looking.

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There is a large conceptual difference here. Many of the hypotheses produced by data mining will not be very meaningful, and some will be almost totally disconnected from any use or value. Most, however, will be more or less useful. This means that with data mining, the inquirer has a fairly comprehensive set of ideas, connections, influences, and so on. The job then is to make sense of, and find use for, them. Statistical analysis required the inquirer first to devise the ideas, connections, and influences to test.

Data mining explores the relationships that exist between these objects. The precise definition of objects is another philosophical issue that need not concern miners. It is almost, if not actually, impossible to define what an object “really” is. ” The miner takes a pragmatic view of the objects in the world, finding it unnecessary to define the actual objects and instead regarding an object as a collection of features about which measurements can be taken. A car, for instance, is accepted by the miner as a defined object.

Since there are likely to be as many measurements short as there are long, such errors also tend to cluster about the “correct” point. Statisticians have devised many ways to characterize this type of error, although the details are not needed here. If the calibration is in error—say, wrong ruler length—this leads to a systematic error, since all measurements made with a given ruler tend to be “off” the mark by the same amount. This is described as a bias. 1 shows the distortion, or error, that might be caused by the “fuzz” in such measurements.

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