liftlift of the rule is defined as the ratio of observed support to the support expected in the case the elements of the rule were independent for the previous set of transactions if the rule is defined as (ry}then the lift of the rule is defined aslift (ry supp supp supp ( frequent itemsetfrequent itemsets are itemsets whose support is greater than user defined support threshold fp growth the most famous algorithm for association rule-mining is the apriori algorithmfor which you will find lot of code and resources on the web and in standard data mining literature howeverhere we will use different and more efficient algorithmthe fp growth algorithm for finding our association rules the major bottleneck in any association rule-mining algorithm is the generation of frequent itemsets if the transaction dataset is having unique productsthen potentially we have possible itemsets the apriori algorithm will first generate these itemsets and then proceed to finding the frequent itemsets this limitation is huge performance bottleneck as even for around unique products the possible number of itemsets is huge this limitation makes the apriori algorithm prohibitively computationally expensive the fp growth algorithm is superior to apriori algorithm as it doesn' need to generate all the candidate itemsets the algorithm uses special data structure that helps it retains itemset association information an example of the data structure is depicted in figure - figure - an example of an fp-tree