# 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. |