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Mainul Hassan, profile picture
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Apriori algorithm

The apriori algorithm is a crucial method for mining frequent itemsets for boolean association rules by iteratively generating candidate itemsets and determining which are frequent based on support count. It highlights that every subset of a frequent itemset must also be frequent and describes the process of candidate generation, pruning, and association rule creation. Despite its advantages, such as the ability to handle large itemsets and ease of implementation, the algorithm has drawbacks like high memory usage and multiple database scans.

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THE APRIORI ALGORITHM PRESENTED BY MAINUL HASSAN
INTRODUCTION The Apriori Algorithmis an influential algorithm for mining frequent itemsets for boolean association rules Some key points in Apriori algorithm – • To mine frequent itemsets from traditional database for boolean association rules. • A subset of frequent itemset must also be frequent itemsets. For example, if {l1, l2} is a frequent itemset then {l1}, {l2} should be frequent itemsets. • An iterative way to find frequent itemsets. • Use the frequent itemsets to generate association rules.
CONCEPTS • A set of all items in a store • A set of all transactions (Transactional Database T) • Each is a set of items s.t. • Each transaction has a transaction ID (TID). Apriori algorithm divided into 3 sections as – },....,,{ 21 miiiI  },....,,{ 21 NtttT  it lt  it Initial frequent itemsets Candidate generation Support calculation Candidate pruning
CONCEPTS • Uses level wise search where k itemsets are use to explore (k+1) itemset. • Frequent subsets are extended one item at a time, which is known as candidate generation process. • Groups of candidates are texted against the data. • It identifies the frequent individual items in the database and extends them to larger and larger itemsets as long as those itemsets appear sufficiently often in the database. • Apriori algorithm determines frequent itemset to determine association rules. • All infrequent itemsets can be pruned if it has an infrequent subset.
THE APRIORI ALGORITHM – PSEDUO CODE o Join Step: is generated by joining with itself. o Prune Step: Any (k-1) itemset that is not frequent cannot be a subset of a frequent k itemset o Pseduo – Code: : candidate itemset of size k : frequent itemset of size k = {frequent items}; for (k = 1; != ; k++) do begin candidate key generated from for each transaction t in database do increment the count of all candidates in that are contained in t = candidate in with min_support end return kC 1kL kC kL 1L kL  1kC kL 1kC 1kL 1kC kk L
HOW THE ALGORITHM WORKS 1. We have to build candidate list for k itemsets and extract a frequent list of k-itemsets using support count. 2. After that we use the frequent list of k itemsets in determining the candidate and frequent list of k+1 itemsets. 3. We use pruning to do that. 4. We repeat until we have an empty candidate or frequent support of k itemsets. 5. Then return the list of k-1 itemsets.
EXAMPLE OF APRIORI ALGORITHM Consider the following Transactional Database – Setp 1: Minimum support count = 2 TID Items T100 1 2 3 T200 2 3 5 T300 1 2 3 5 T400 2 5 T500 1 3 5 itemse ts Support {1} 3 {2} 3 {3} 4 {4} 1 {5} 4 Candidate itemset -1 Frequent itemset -1 itemse ts Support {1} 3 {2} 3 {3} 4 {5} 4 prune Because minimum support count is 2
EXAMPLE OF APRIORI ALGORITHM Step 2: itemse ts suppor t {1, 2} 1 {1, 3} 3 {1, 5} 2 {2, 3} 2 {2, 5} 3 {3, 5} 3 TID Items T100 1 2 3 T200 2 3 5 T300 1 2 3 5 T400 2 5 T500 1 3 5 Candidate itemset -2 itemse ts Support {1, 3} 3 {1, 5} 2 {2, 3} 2 {2, 5} 3 {3, 5} 3 Frequent itemset - 2 prune Database
EXAMPLE OF APRIORI ALGORITHM Step 3: itemsets In FI2? {1, 2, 3} {1, 2}, {1, 3}, {2, 3} No {1, 2, 5} {1, 3}, {1, 5}, {2, 5} Yes {1, 3, 5} {1, 3}, {1, 5}, {3, 5} No {2, 3, 5} Yes TID Items T100 1 2 3 T200 2 3 5 T300 1 2 3 5 T400 2 5 T500 1 3 5 Candidate itemset -3 itemse ts support {1, 3, 5} 2 {2, 3, 5} 2 Frequent itemset - 3 itemse ts Support {1, 3} 3 {1, 5} 2 {2, 3} 2 {2, 5} 3 Frequent itemset - 2 Don’t match Remember .. A subset of frequent itemset must also be frequent itemsets Database Same as other two itemsets
EXAMPLE OF APRIORI ALGORITHM Step 4: itemsets suppor t {1, 2, 3, 5} 1 TID Items T100 1 2 3 T200 2 3 5 T300 1 2 3 5 T400 2 5 T500 1 3 5 Candidate itemset -4 itemse ts Support Empty Frequent itemset - 4 prune Database itemsets In FI -3 {1, 2, 3, 5} {1, 2, 3 }, {1, 2, 5}, {1, 3, 5}, {2, 3, 5} No itemse ts support {1, 3, 5} 2 Frequent itemset - 3 Don’t match Remember .. A subset of frequent itemset must also be frequent itemsets Candidate itemset -4 The itemsets is empty so Split
APRIORI ALGORITHM • Advantages • Uses large itemsets property • Easily parallelized • Easy to implement • Disadvantages • Assumes transaction database is memory resident. • Requires many database scans.
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