TAILIEUCHUNG - Data Mining and Knowledge Discovery Handbook, 2 Edition part 14

Data Mining and Knowledge Discovery Handbook, 2 Edition part 14. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 110 Ying Yang Geoffrey I. Webb and Xindong Wu Dynamic-qualitative discretization The above mentioned methods are all time-insensitive while dynamic-qualitative discretization Mora et al. 2000 is typically time-sensitive. Two approaches are individually proposed to implement dynamic-qualitative discretization. The first approach is to use statistical information about the preceding values observed from the time series to select the qualitative value which corresponds to a new quantitative value of the series. The new quantitative value will be associated to the same qualitative value as its preceding values if they belong to the same population. Otherwise it will be assigned a new qualitative value. To decide if a new quantitative value belongs to the same population as the previous ones a statistic with Student s t distribution is computed. The second approach is to use distance functions. Two consecutive quantitative values correspond to the same qualitative value when the distance between them is smaller than a predefined threshold significant distance. The first quantitative value of the time series is used as reference value. The next values in the series are compared with this reference. When the distance between the reference and a specific value is greater than the threshold the comparison process stops. For each value between the reference and the last value which has been compared the following distances are computed distance between the value and the first value of the interval and distance between the value and the last value of the interval. If the former is lower than the latter the qualitative value assigned is the one corresponding to the first value. Otherwise the qualitative value assigned is the one corresponding to the last value. Ordinal discretization Ordinal discretization Frank and Witten 1999 Macskassy et al. 2001 as its name indicates conducts a transformation of quantitative data that is able to preserve their ordering .

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