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Data Mining and Knowledge Discovery Handbook, 2 Edition part 28

Data Mining and Knowledge Discovery Handbook, 2 Edition part 28. 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. | 250 Jerzy W. Grzymala-Busse independent variables and the decision is a dependent variable. A very simple example of such a table is presented as Table 13.1 in which attributes are Temperature Headache Weakness Nausea and the decision is Flu. The set of all cases labeled by the same decision value is called a concept. For Table 13.1 case set 1 2 4 5 is a concept of all cases affected by flu for each case from this set the corresponding value of Flu is yes . Table 13.1. An Example of a Dataset. Case Attributes Temperature Headache Weakness Nausea Decision Flu 1 veryJiigli yes yes no yes 2 high yes no yes yes 3 normal no no no no 4 normal yes yes yes yes 5 high no yes no yes 6 high no no no no 7 normal no yes no no Note that input data may be affected by errors. An example of such a data set is presented in Table 13.2. The case 7 has value 42.5 for Weakness an obvious error since the attribute Weakness is symbolic with possible values yes and no. Such errors must be corrected before rule induction. Table 13.2. An Example of an Erroneous Dataset Case Attributes Temperature Headache Weakness Nausea Decision Flu 1 veryJiigli yes yes no yes 2 high yes no yes yes 3 normal no no no no 4 normal yes yes yes yes 5 high no yes no yes 6 high no no no no 7 normal no 42.5 no no Another problem is caused by numerical attributes for example Temperature may be represented by real numbers as in Table 13.3. Obviously numerical attributes must be converted into symbolic attributes before or during rule induction. The process of converting numerical attributes into symbolic attributes is called discretization or quantization . 13 Rule Induction 251 Table 13.3. An Example of a Dataset with a Numerical Attribute. Case Attributes Temperature Headache Weakness Nausea Decision Flu 1 41.6 yes yes no yes 2 39.8 yes no yes yes 3 36.8 no no no no 4 37.0 yes yes yes yes 5 38.8 no yes no yes 6 40.2 no no no no 7 36.6 no yes no no Input data may be incomplete i.e. some attributes may have missing .