Data Mining
A data model defines the structure of the data for the purpose of communicating between functional and technical people to show data needed for business processes, or for communicating a plan to develop how data is stored and accessed among application development team members.
The process of analyzing multidimensional data using three operations: consolidation (the aggregation of available), drill-down (the ability for users to see the underlying details), and slice and dice (the ability for users to select subsets and view them from different perspectives).
A modeling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure.
-Behavior Trees are a formal, graphical modeling language used primarily in systems and software engineering.
-A flowchart is a schematic representation of an algorithm or a stepwise process.
-Behavior Trees are a formal, graphical modeling language used primarily in systems and software engineering.
-A flowchart is a schematic representation of an algorithm or a stepwise process.
Data integration is the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information. A complete data integration solution delivers trusted data from a variety of sources.
In data preparation, you will prepare the data for the next stage i.e. the modeling stage. What you do here is influenced by the choice of technique you use in the next stage.
But some things are done in most cases – for example, identifying missing values and treating them, identifying outlier values (unusual values) and treating them, transforming variables, creating binary variables if required, etc.
This is the stage where you will partition the data as well i.e. create training data (to do modeling) and validation (to do validation).
But some things are done in most cases – for example, identifying missing values and treating them, identifying outlier values (unusual values) and treating them, transforming variables, creating binary variables if required, etc.
This is the stage where you will partition the data as well i.e. create training data (to do modeling) and validation (to do validation).