Choosing an Algorithm by Task
To help you select an algorithm for use with a specific task, the following table provides suggestions for the types of tasks for which each algorithm is traditionally used. +
Examples of tasks
Microsoft algorithms to use
Predicting a discrete attribute:
Flag the customers in a prospective buyers list as good or poor prospects.
Calculate the probability that a server will fail within the next 6 months.
Categorize patient outcomes and explore related factors.
Predicting a continuous attribute:
Forecast next year's sales.
Predict site visitors given past historical and seasonal trends.
Generate a risk score given demographics.
Predicting a sequence:
Perform clickstream analysis of a company's Web site.
Analyze the factors leading to server failure.
Capture and analyze sequences of activities during outpatient visits, to formulate best practices around common activities.
Finding groups of common items in transactions:
Use market basket analysis to determine product placement.
Suggest additional products to a customer for purchase.
Analyze survey data from visitors to an event, to find which activities or booths were correlated, to plan future activities.
Finding groups of similar items:
Create patient risk profiles groups based on attributes such as demographics and behaviors.
Analyze users by browsing and buying patterns.
Identify servers that have similar usage characteristics.
The following table provides links to learning resources for each of the data mining algorithms that are provided in SQL Server Data Mining: +
Basic algorithm description
Explains what the algorithm does and how it works, and outlines possible business scenarios where the algorithm might be useful.
Provides technical detail about the implementation of the algorithm, with academic references as necessary. Lists the parameters that you can set to control the behavior of the algorithm and customize the results in the model. Describes data requirements and provides performance tips if possible.
Explains how information is structured within each type of data mining model, and explains how to interpret the information stored in each of the nodes.
Data mining queries
Provides multiple queries that you can use with each model type. Examples include content queries that let you learn more about the patterns in the model, and prediction queries to help you build predictions based on those patterns.
Determine the algorithm used by a data mining model
Create a Custom Plug-In Algorithm
Explore a model using an algorithm-specific viewer
View the content of a model using a generic table format
Learn about how to set up your data and use algorithms to create models