Frequent itemset generation |
BSc in Computer Science |
Draws the search space of frequent itemsets. Computes the support of an itemset. Generates candidate itemsets using the Apriori principle. Applies the Apriori method to find frequent itemsets. |
Implements a method for frequent itemset generation. Applies Apriori and a depth-first method or FP-Growth to find frequent itemsets. |
Compares and contrasts breadt-first (Apriori), depth-first, and FP-Growth methods |
Compact representations of itemsets |
Frequent itemset generation |
Generates closed itemsets and maximal itemsets from frequent itemsets. Generates frequent itemsets from closed or maximal frequent itemsets. Explains why they are useful. |
Adapts the Apriori algorithm (or another frequent itemset algorithm) to directly produce closed itemsets and maximal itemsets efficiently. |
Finds non-redundant or non-derivable frequent itemsets. |
Association rules and evaluation of patterns |
Frequent itemset generation, elementary probability calculus |
Computes the confidence of association rules and generates association rules from frequent itemsets. |
Describes and computes two different measures of association rule interestingness. |
Analyses statistical significance of association rules. Finds non-redundant sets of association rules. |
Non-binary attributes in frequent patterns |
Frequent itemset generation |
Binarises categorical and continuous attributes for frequent patterns. Performs other pre-processing on the data. |
Adapts the Apriori algorithm and candidate generation to incorporate non-binary attributes efficiently. |
Finds negative patterns. |
The general Apriori principle |
Frequent itemset generation |
Finds sequential patterns or subgraph patterns. Recognizes if a given problem can be formulated as frequent pattern mining problem. |
Formulates new frequent pattern classes. Draws the search space for them. Derives candidate generation methods for the Apriori algorithm and implements them. |
Applies non-trivial frequent pattern mining on a real problem. |