TAILIEUCHUNG - Data Preparation for Data Mining- P11

Data Preparation for Data Mining- P11: 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. | separately from the effects of the remaining frequencies. While it is possible to construct complex mathematical structures to perform the necessary filtering the purpose behind filtering is easy to understand and to see. Figure showed the spectrum of a trended waveform. Almost all of the power in this spectrum occurs at the lowest frequency which is 0. With a frequency of 0 the corresponding waveform to that frequency doesn t change. And indeed that is a linear trend an unvarying increase or decrease over time. At each uniform displacement the trend changes by a uniform amount. Removing trend corresponds to low-frequency filtering at the lowest possible frequency 0. If the trend is retained it is called low-pass filtering as the trend the low-frequency component is passed through the filter. If the trend is removed it would be called high-pass filtering since all frequencies but the lowest are passed through the filter. In addition to the zero frequency component there are an infinite number of possible low-frequency components that are usefully identified and removed from series data. These components consist of fractional frequencies. Whereas a zero frequency represents a completely unvarying component a fractional frequency simply represents a fraction of the whole cycle. If the first quarter of a sine wave is present in a composite waveform for example that component would rise from 0 to 1 and look like a nonlinear trend. Some of the more common fractional frequency components include exponential growth curves logistic function curves logarithmic curves and power-law growth curves as well as the linear trend already discussed. Figure illustrates several common trend lines. Where these can be identified and a suitable underlying generating mechanism proposed that mechanism can be used to remove the trend. For instance taking the logarithm of all of the series values for modeling is a common practice for some series data sets. Doing this removes the .

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