By Dorian Pyle
I've got loads of adventure getting ready facts for research. i used to be trying to find a publication that might upload to my knowing of and increase my association for facts training. this isn't that publication. At most sensible, the publication presents perception into the kinds of concerns confronted in getting ready info and emphasizes the worth of such. instead of criticize, I desire to foreworn those that have already practiced at a a bit of rigorous point (more than 5 semesters of statistics/data mining) that this is able to now not be what you're looking.
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Additional info for Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems)
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.
These crucial assumptions underpinning data exploration and data mining are usually unstated. They do, however, have a major impact on the actual process of mining data, and they affect how data is prepared for mining. Any analysis of data that is made in the hope of either understanding or influencing the world makes these assumptions. ” Chapter 1 provided an overall framework for data exploration and put all of the components into perspective. This chapter will focus on the nature of the connection between the experiential world and the measurements used to describe it, how those measurements are turned into data, and how data is organized into data sets.
S. ” The first question is not always immediately seen as irrelevant, whereas the second is. Some companies seem to have the impression that in order to produce effective models, knowledge of the data and the problem are not really required, but that the tools will do all the work. Where this myth came from is hard to imagine. It is so far from the truth that it would be funny if it were not for the fact that major projects have failed entirely due to ignorance on the part of the miner. Not that the miner was always at fault.
Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems) by Dorian Pyle