Data mining algorithms and their use cases

Hi folks,

I have compiled the comparisons and use case of OOB algorithms available within SSAS/DM for handy reference.

Name	Description	Type	Use cases	Parent algorithm	Supported Values
Associate Rule	Builds rules describing which items are most likely to be  appear together in a transaction.	Association	The rules can be used to predict the presence of an item  based on the presence of other items in a transaction.	-	-
Clustering	Uses iterative techniques to group records from a dataset  into clusters containing similar characteristics.	Clustering	This is useful when you want to find general groupings in  your data.	-	-
Sequence Clustering	Combination of sequence analysis and clustering, which  identifies clusters of similarly ordered events in a sequence	Clustering/ Sequencing	Clusters can be used to predict the likely ordering of  events in a sequence based on known characteristics.	Clustering	-
Decision Trees	It’s a classification algorithm.	Classification	Works well for predictive modeling	-	Supports the prediction of both discrete and continuous  attributes.
Linear Regression	This algorithm is a particular configuration of the  Microsoft Decision Trees algorithm, obtained by disabling splits (the whole  regression formula is built in a single root node).	Regression	Works well for regression modeling	Decision Trees	The algorithm supports the prediction of continuous  attributes.
Time Series	algorithm uses a combination of ARIMA analysis and linear  regression based on decision trees to analyze time-related data, such as  monthly sales data or yearly profits.	Classification/ Regression	Discovered patterns can be used to predict values for  future time steps. The algorithm can be customized to use either the decision  tree method, ARIMA, or both.	Linear Regression	-
Naive Bayes	Classification algorithm that is quick to build.	Classification	Works well for predictive modeling	-	The algorithm supports only discrete attributes.
Neural Network	Uses a gradient method to optimize parameters of  multilayer networks to predict multiple attributes	Classification/ Regression	It can be used for classification of discrete attributes  as well as regression of continuous attributes.	-	Discrete/Continuous
Logistic Regression	This algorithm is a particular configuration of the  Microsoft Neural Network algorithm, obtained by eliminating the hidden layer	Regression	Works well for regression modeling	Neural Network	Supports the prediction of both discrete and continuous  attributes.