Chapter 15. Preprocessing 1. Constructing metadata and automatic variable selection Description The process illustrates, by using the dataset, how to generate the metadata of a dataset by the Spambase DMDB operator, then how automatic variable selection can be obtained by the operator. The Variable Selection dataset contains attributes, one of which is the binary target. In order to visualize a dataset, it may Spambase 58 be necessary to determine the most important input attributes which can be used in the graphical representation. [] Spambase Output The operator produces such metadata (descriptive statistics) as mean, variance, minimum, maximum, DMDB skewness, and kurtosis. In case of discrete attributes these are complemented by the mode. Figure 15.1. Metadata produced by the operator DMDB The default settings of the operator are applied except that the minimum R-square is Variable Selection increased in to filter the unnecessary attributes. Figure 15.2. The settings of operator Variable Selection The result on the one hand will be a list contains the decision about the variables, i.e., whether it remains or not in the data mining process, on the other hand, a few graphs of the importance of the variables. Figure 15.3. List of variables after the selection Figure 15.4. Sequential R-square plot In view of the important variables a number of graphical tools of the Enterprise Miner can be used to display ™ the records. Figure 15.5. The binary target variables in a function of the two most important input attributes after the variable selection Interpretation of the results
Chapter 15. Preprocessing 1. Constructing metadata and automatic variable selection Description The process illustrates, by using the Spambase dataset, how to generate the metadata of a dataset by the DMDB operator, then how automatic variable selection can be obtained by the Variable Selection operator. The Spambase dataset contains 58 attributes, one of which is the binary target. In order to visualize a dataset, it may be necessary to determine the most important input attributes which can be used in the graphical representation. Spambase [] Output The DMDB operator produces such metadata (descriptive statistics) as mean, variance, minimum, maximum, skewness, and kurtosis. In case of discrete attributes these are complemented by the mode. Figure 15.1. Metadata produced by the DMDB operator The default settings of the Variable Selection operator are applied except that the minimum R-square is increased in to filter the unnecessary attributes. Figure 15.2. The settings of Variable Selection operator The result on the one hand will be a list contains the decision about the variables, i.e., whether it remains or not in the data mining process, on the other hand, a few graphs of the importance of the variables. Figure 15.3. List of variables after the selection Figure 15.4. Sequential R-square plot In view of the important variables a number of graphical tools of the Enterprise Miner ™ can be used to display the records. Figure 15.5. The binary target variables in a function of the two most important input attributes after the variable selection Interpretation of the results
2011-0103_06_case_studies_in_data_mining.docx