Literature review

An itemset is frequent if its support satisfies at least the minimum support, otherwise it is said to be infrequent. A frequent itemset is a Maximal Frequent itemset if it is a frequent set and no superset of this is a frequent set. The paper aims to find the Maximal Frequent itemset from a huge data source. 3.

Definition. Frequent itemsets (Agrawal et al., 1993, 1996) are a form of frequent pattern. Given examples that are sets of items and a minimum frequency, any set of items that occurs at least in the minimum number of examples is a frequent itemset. In such more general settings, the term frequent pattern is often used.

• Apriori algorithm. Apriori (Agrawal and Srikant 1994) is an algorithm that mines frequent itemsets for generating Boolean association rules.
• FP-Growth algorithm.
• TreeProjection algorithm.
• COFI algorithm.
• TM algorithm.
• P-Mine algorithm.
• LP-Growth algorithm.
• EXTRACT algorithm.

Definition: It is a frequent itemset that is both closed and its support is greater than or equal to minsup. An itemset is closed in a data set if there exists no superset that has the same support count as this original itemset.

Then what are closed and maximal frequent itemsets? By definition, An itemset is maximal frequent if none of its immediate supersets is frequent. An itemset is closed if none of its immediate supersets has the same support as the itemset.

We construct a link for every single node in the FP-tree, and then give the closed frequent itemsets and the pruning of the link. This algorithm is an algorithm which only scans the database once, also it avoids the defect about patterns tree based on terms and can mine the closed frequent items efficiently.

Q18. Which of the following is direct application of frequent itemset mining? Q19.

Apriori algorithm uses the candidate generation through which the frequent item sets are generated. FP-growth (frequent pattern growth) uses a prefix-tree (FP-tree) structure to store the database in a compressed form. FP-growth adopts a divide-and-conquer strategy for finding the frequent item sets.

A closed pattern is a frequent pattern. So it meets the minimum support criteria. In addition to that, all super-patterns of a closed pattern are less frequent than the closed pattern. Let’s see some examples. Suppose, the minimum support count is 2.

X is said to be a max-pattern if X is a frequent pattern and there exists no frequent super pattern Y (where Y is a super set of X). On the other hand, X is said to be a closed-pattern if X is frequent and there exits no super pattern Y (where Y is a super set of X) with the same support as X.

Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

Apriori algorithm was the first algorithm that was proposed for frequent itemset mining. It was later improved by R Agarwal and R Srikant and came to be known as Apriori. This algorithm uses two steps “join” and “prune” to reduce the search space. It is an iterative approach to discover the most frequent itemsets.

The algorithm gets terminated when the frequent itemsets cannot be extended further. The advantage is that multiple scans are generated for candidate sets. The disadvantage is that the execution time is more as wasted in producing candidates everytime, it also needs more search space and computational cost is too high.

Steps for Apriori Algorithm Step-1: Determine the support of itemsets in the transactional database, and select the minimum support and confidence. Step-2: Take all supports in the transaction with higher support value than the minimum or selected support value.

The Apriori algorithm is used for mining frequent itemsets and devising association rules from a transactional database. The parameters “support” and “confidence” are used. Support refers to items’ frequency of occurrence; confidence is a conditional probability. Items in a transaction form an item set.

Suppose A^B -> C then Confidence = support(A^B->C) i.e. a number of transactions in which all three items are present / support(A,B) i.e. a number of transactions in which both A and B are present. So the answer is confidence= support(2,5,3)/support (2,5)

A confidence of 60% means that 60% of the customers, who purchased milk and bread also bought butter. So here, by taking an example of any frequent itemset, we will show the rule generation. So if minimum confidence is 50%, then first 3 rules can be considered as strong association rules.

Apriori algorithm is a classical algorithm in data mining. It is used for mining frequent itemsets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store.

Because the order of the time complexity is exponential, the a priori algorithm runs slowly because as many as the number of attributes used increases, the time complexity is longer. On the other hand, the time complexity of evolutionary algorithms follows a quadratic distribution O(n2).

In particular, Apriori is one of the most used algorithms for finding frequent itemsets using candidate generation. It is characterized as a level-wise search algorithm using antimonotonicity of itemsets. Let the set of frequent itemsets of size k be Fk and their candidates be Ck.