tailieunhanh - Data Preparation for Data Mining- P9

Data Preparation for Data Mining- P9: Ever since the Sumerian and Elam peoples living in the Tigris and Euphrates River basin some 5500 years ago invented data collection using dried mud tablets marked with tax records, people have been trying to understand the meaning of, and get use from, collected data. More directly, they have been trying to determine how to use the information in that data to improve their lives and achieve their objectives. | allocated for squashing out-of-range values is highly exaggerated to illustrate the point. Figure The transforms for squashing overrange and underrange values are attached to the linear part of the transform. This composite S -shaped transform translates most of the values linearly but also transforms any out-of-range values so that they stay within the 0-1 limits of the range. This sort of S curve can be constructed to serve the purpose. Writing computer code to achieve this is somewhat cumbersome. The description shows very well the sort of effect that is needed but fortunately there is a much easier and more flexible way to get there. Softmax Scaling Softmax scaling is so called because among other things it reaches softly toward its maximum value never quite getting there. It also has a linear transform part of the range. The extent of the linear part of the range is variable by setting one parameter. It also reaches softly toward its minimum value. The whole output range covered is 0-1. These features make it ideal as a transforming function that puts all of the pieces together that have been discussed so far. The Logistic Function It starts with the logistic function. The logistic function can be modified to perform all of the work just described and when so modified it does it all at once so that by plugging in a variable s instance value out comes the required transformed value. An explanation of the workings of the logistic function is in the Supplemental Material section at the end of this chapter. Its inner workings are a little complex and so long as what needs to be done is clear getting to the squashing S curve understanding the logistic function itself is not necessary. The Supplemental Material can safely be skipped. Please purchase PDF Split-Merge on to remove this watermark. The explanation is included for interest since the same function is an integral part of neural networks mentioned in Chapter 10. The Supplemental .