1. Find all Frequent Itemsets. 

2. G s from the Frequent Itemsets. enerate Strong Association Rule 2.3 The Apriori Algorithm Apr The item i)
 as a pre-defined minimum support count. 

iori is an influential algorithm for mining frequent itemsets for Boolean association rules [1]. 

 name of the algorithm is based on the fact that the algorithm uses prior knowledge of frequent set properties. Association rule mining is a two steps process [7]. 

Find all frequent itemsets: By definition, each of these itemsets will occur at least as frequently ii) Generate strong Association rules from the frequent itemsets: By definition, these rules must satisfy minimum confidence. 

iori employees an iterative approach known as a le to explore *(k+1)*-itemsets. First, the set of frequent 1-itemsets is found. This set is denoted L1.

s used t more frequent k-itemsets can be found. The finding of each Lk requires one full scan of the base. An important property called Apriori property, based on the observation is that, if an set I is not frequent, that is, P(I ) < min_sup then if an item A
resulting itemset (i.e., I  A) cannot occur more frequently than I. Therefore, I  A is not uent either, that is, P(I A) < 
Apr vel-wise search, where k-itemsets are used L1 i o find L2, the set of frequent 2-itemsets, which is used to find L3, and so on, until no data item is added to the itemset I, the freq *min_sup*. To understand how Apriori property is used in the algorithm, let us look at how *Lk-1* is used to find Lk
. A two-step process is followed, consisting of join and prune actions. 

The join step: To find L a et of candidate k-itemsets is generated by joining *Lk-1* with itself. This set of candidates is denoted by Ck
. Let l1 and l2 be itemsets in *Lk-1* then l1 and l2 are joinable if
(l The prune step count of each candid than minim riori property. Any *(k-1)*-itemset that et of a frequent k-itemset. Hence if any *(k-1)*-subset of a candidate k-items , then the candidate cannot be frequent k
, s their first *(k-2)* items are in common, i.e., (l1[1]=l2[1])  (l1[2]=l2[2]) ….. (l1[k-2]=l2[k-2]) 
1[k-1]<l2[k-1]). 

: Ck is the superset of Lk
. A scan of the database to determine the ate in Ck would result in the determination of Lk (itemsets having a count no less um support in Ck
). But this scan and computation can be reduced by applying the Ap is not frequent cannot be a subs et is not in *Lk-1* either and so can be removed from Ck
..

## 2.4 Illustration Of Apriori Algorithm

Consider an example of Apriori, based on the following transaction database, D of Figure: 1, with 9 transactions, to illustrate Apriori algorithm. In the first iteration of the algorithm, each item is a member of the set of candidate 1-itemsets, C1 The algorithm simply scans all of the transactions in order to count the number of occurrences of each item. If minimum support count is set to 2, frequent 1-itemsets, *L1,* can then be determined from candidate 1-itemsets satisfying minimum support [6]. 

To discover the set of frequent 2-itemsets, *L2, the* algorithm uses L1 | L1 to generate a candidate set of 2-itemsets (Figure: 4). The transactions D are scanned and the support coun accum t of each candidate itemset in C2 is ulated (Figure: 5). 