DataMinCardinalityn
DataMinCardinalityn is a concept in the field of data mining and machine learning, specifically within the realm of association rule learning. It refers to the minimum number of items that must be present in an itemset for the itemset to be considered valid or interesting. In other words, it is a threshold that filters out itemsets with fewer than n items, helping to reduce the computational complexity and focus on more meaningful patterns.
The cardinality of an itemset is simply the number of items it contains. For example, in a
Setting an appropriate DataMinCardinalityn is crucial for the efficiency and effectiveness of data mining algorithms. A
DataMinCardinalityn is often used in conjunction with other parameters such as DataMinSupport and DataMinConfidence to control