Join Ron Davis for an in-depth discussion in this video Key concepts, part of Learning Excel Data-Mining.
- Key concepts.…The mining structure.…The mining structure defines the data…from which we are building the mining model.…The structure will specify the source data,…the number, and types of columns,…and optionally, a partition of the data…into training and testing sets.…A single structure can support multiple mining models…that share the same problem domain.…Depending on how the mining structure is created,…it can contain bindings to your data sources,…so that the structure can be read and reprocessed…without you needing to provide the same data again.…
This is a very useful feature.…Additionally, a mining structure contains all of the models…that are used to analyze the source data of a structure.…The columns in a mining structure…do not represent attributes.…They are simply columns of data that will be available…in creating models.…Frequently, a mining structure contains more columns…than will be used in any one model.…You may have multiple models sharing some columns…and using some specifically.…
Attributes.…An attribute is the most basic part…
- Recognize the factors involved in building an environment.
- Define models, induction, and prediction.
- Determine key concepts in Excel data-mining.
- Analyze key influencers.
- Break down how to utilize the detect categories tool.
- Recognize where and when to use the Shopping Basket Analysis.
Skill Level Intermediate
Querying Microsoft SQL Server 2012with Gerry O'Brien5h 34m Beginner
Cleaning Up Your Excel 2013 Datawith Dennis Taylor1h 59m Intermediate
1. Data-Mining Concepts
2. The Microsoft Data-Mining Algorithms
The Microsoft algorithms3m 32s
3. Data Mining between Excel and SQL Server Analysis Services
Understanding mining models2m 33s
4. Using the Data-Mining Add-ins in Excel
Next steps2m 32s
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