- Approximate Distinct Count : to quickly estimates the number of distinct values
- Approximate Percentile : to computes approximate percentiles
- Correlation : to determine if one variable is useful for predicting an other
- Generalized Linear Regression & Prediction : to perform linear regression analysis
- Principal Component Analysis : for dimensionality reduction
- Simple | Weighted | Exponential Moving Average : compute average with special algortihm
- K-Nearest Neighbor : classification algorithm based on proximity
- Support Vector Machines : build a SVM model and do prediction
- Confusion Matrix [Plot] : visualize ML algorithm performance
- Kmeans : famous clustering algorithm
- Minhash : Another clustering technic which depends on the set of products bought by users
- Naïve Bayes : useful classification method especially for documents
- Random Forest Functions : predictive modelling approaches broadly used for supervised classification learning

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## Friday, March 28, 2014

### Machine Learning with Aster !

I am now working with Aster to do Machine Learning and statistics. Here are the functions you can use :

Labels:
Analytics,
Aster,
Business Intelligence,
Machine Learning,
Statistics

Location:
Antony, France

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