|Basket_Generator||Generates baskets (sets) of items that occur together in data records (typically transaction records or web page logs).|
|CFilter||General-purpose collaborative filter. Helps discover which items or events are frequently paired with other items or events.|
|FPGrowth||Uses an FP-growth algorithm to generate association rules from patterns in a data set and then determines their interestingness.|
|Recommender Functions||The recommender functions include the following:
WSRecommender is an item-based, collaborative filtering function that uses a weighted-sum algorithm to make recommendations (such as items for users to consider buying).
KNNRecommenderTrain and KNNRecommenderPredict take a similar approach to WSRecommender, but attempt to increase prediction accuracy by adjusting for systematic biases and replacing heuristic calculations of similarity coefficients with a global optimization that simultaneously estimates all weights.