association rule mining example

        association rule mining example

In this tutorial learn to association rule mining example previous tutorial what is data preprocessing follow this link: what is data  preprocessing

Association Rule Mining:

• Association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases.

 • It is intended to identify strong rules discovered in databases using different measures of interestingness.

 • Based on the concept of strong rules, Rakesh Agrawal et al. introduced association rules.

Problem Definition:

   The problem of association rule mining is defined as: Let I = {11,12,..., in}be a set of nbinary attributes called items.

 Let D = {ti,t2...., tm}be a set of transactions called the database.

Each transaction in Dhas a unique transaction ID and contains a subset of the items in I.

  A rule is defined as an implication of the form X Y where X, Y CI and X Y = 0.

    The sets of items (for short itemsets) Xand Yare called antecedent (left-hand-side or LHS) and consequent (right-hand-side or RHS) of the rule respectively. Example: To illustrate the concepts, we use a small example from the supermarket domain.

     The set of items is 1 = {milk, bread, butter, beer) and a small database containing the items (1 codes presence and 0 absence of an item in a transaction) is shown in the table.

An example rule for the supermarket could be butter, bread} = {milk} meaning butter and bread are bought, customers also buy milk.

 Important concepts of Association Rule Mining:

The support Supp lofam itemset X is defined as the proportion of transactions in the data set which contain the itemset. In the example database, the itemset

milk, bread, butter)has a support of 1/5 = 0.2 since it occurs in 20% of all bansactions (I out of 5 transactions

The confidenceof a rule is defined

conf( X = Y) = supp(.XUY)/supp(X).

For example the rule (butter, bread} = {milk) has a confidence of 0.2702 = 1.0 in the database. which means that for 100% of the transactions containing butter and bread the rule is correct (100% of the times a customer buys butter and bread, milk is bought as well). Confidence can be interpreted as an estimate of the probability P Y ), the probability of finding the RHS of the rule in transactions under the condition that these transactions also contain the LHS

The fofa rule is defined as