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FAME 10 provides several enhanced features for creating object models, including Support for longer object names (up to 242 characters) and for assigning an unlimited number of user-defined attributes to an object; Support for object names with up to 35 dimensions; December 2010: FAME 10.1 released. December 2011: FAME 10.2 released.
However, SAS Institute clearly states that SEMMA is not a data mining methodology, but rather a "logical organization of the functional toolset of SAS Enterprise Miner." A review and critique of data mining process models in 2009 called the CRISP-DM the "de facto standard for developing data mining and knowledge discovery projects."
The DATA step has executable statements that result in the software taking an action, and declarative statements that provide instructions to read a data set or alter the data's appearance. [4] The DATA step has two phases: compilation and execution. In the compilation phase, declarative statements are processed and syntax errors are identified.
Empty values settings per variable, per data set or globally. Assumption checks via export and then plotting of residuals and/or per analyses via tests and plots ( Levene's , Brown-Forsythe, Shapiro–Wilk , Q–Q , Raincloud etc.)
A once-common method of imputation was hot-deck imputation where a missing value was imputed from a randomly selected similar record. The term "hot deck" dates back to the storage of data on punched cards, and indicates that the information donors come from the same dataset as the recipients.
The Global Energy Forecasting Competition (GEFCom) is a competition conducted by a team led by Dr. Tao Hong that invites submissions around the world for forecasting energy demand. [1]
Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [ 1 ]
Values in a data set are missing completely at random (MCAR) if the events that lead to any particular data-item being missing are independent both of observable variables and of unobservable parameters of interest, and occur entirely at random. [5] When data are MCAR, the analysis performed on the data is unbiased; however, data are rarely MCAR.