Itemsets:
The most basic item is a set pattern, which is a collection of a number of items. Frequent itemset mode refers dataset frequently occurring sequences or structures. Frequent itemsets support means is less than the minimum support (min_sup) collection. Which refers to the frequency support is a collection appear in all affairs. Classic applications frequent item sets are shopping basket models. Evaluation criteria commonly used set of frequent items have support, confidence and lift (association rules) three
Association rules:
Association rule is as the implication X → Y, wherein, X and Y are respectively referred to as the pilot (Antecedent or left-hand-side, LHS) and subsequent association rule (the CONSEQUENT or right-hand-side, RHS). Among them, association rules XY, there is support and trust.
: Support gravity several times associated data in the data set appears the total data set
Confidence: After a data appear, the probability of occurrence of another data, or conditional probability data.
Lift: indicates the condition of containing Y, X containing both the probability of the occurrence probability ratio of the overall X
