5 Matching Annotations
  1. Jul 2019
    1. Given a dataset of transactions, the first step of FP-growth is to calculate item frequencies and identify frequent items. Different from Apriori-like algorithms designed for the same purpose, the second step of FP-growth uses a suffix tree (FP-tree) structure to encode transactions without generating candidate sets explicitly, which are usually expensive to generate. After the second step, the frequent itemsets can be extracted from the FP-tree.
  2. Mar 2018
  3. Jan 2018
    1. Whereas normal type classes represent predicates on types (each type is either an instance of a type class or it isn’t), multi-parameter type classes represent relations on types
  4. Sep 2017
  5. Apr 2017
    1. Coupling your data and code adds the additional problem that if you want to use a function at a certain point, you have to find a way get its object to that point.