If sampling is done on the dataset, this can be done by explicitly specifying the size of the sample, or on probability, and also a filter can be used, in the case when the parts of the dataset are not to be represented proportionally in the sample, rather a given subset of the original dataset is necessary for the process. For example, filtering for the records belonging to a given time on every day can be done as follows: Figure 4.5. Preferences for dataset sampling Figure 4.6. Preferences for dataset filtering Interpretation of the results performing the aggregation or sampling, the received dataset will only consist of the aggregate values emerging as a result of the specified operations, or the records that fulfil the specified conditions, respectively: Figure 4.7. Resulting dataset after dataset sampling Figure 4.8. Resulting dataset after dataset filtering Video Workflow preproc_exp2.rmp Keywords aggregation summation sampling data filtering Operators Aggregate Filter Examples Multiply Read CSV Sample 3. Creating and filtering attributes Description The process shows, using dataset, how new, computed The Insurance Company Benchmark (COIL 2000) attributes can be created based on existing data, in case the attributes are not appropriate in their original form,
If sampling is done on the dataset, this can be done by explicitly specifying the size of the sample, or on probability, and also a filter can be used, in the case when the parts of the dataset are not to be represented proportionally in the sample, rather a given subset of the original dataset is necessary for the process. For example, filtering for the records belonging to a given time on every day can be done as follows: Figure 4.5. Preferences for dataset sampling Figure 4.6. Preferences for dataset filtering Interpretation of the results performing the aggregation or sampling, the received dataset will only consist of the aggregate values emerging as a result of the specified operations, or the records that fulfil the specified conditions, respectively: Figure 4.7. Resulting dataset after dataset sampling Figure 4.8. Resulting dataset after dataset filtering Video Workflow preproc_exp2.rmp Keywords aggregation summation sampling data filtering Operators Aggregate Filter Examples Multiply Read CSV Sample 3. Creating and filtering attributes Description The process shows, using The Insurance Company Benchmark (COIL 2000) dataset, how new, computed attributes can be created based on existing data, in case the attributes are not appropriate in their original form,
2011-0103_06_case_studies_in_data_mining.docx