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What are the measures of association rules?

What are the measures of association rules?

An association rule is a condition of the form of X → Y where X ⊆ I and Y ⊆ I are two sets of items. The support of a rule X → Y is the number of transactions that contain both X and Y, while the confidence of a rule X → Y is the number of transactions containing X, that also contain Y.

What are the three measures used in association rules?

There are three common ways to measure association.

• Measure 1: Support. This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears.
• Measure 2: Confidence. This says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}.
• Measure 3: Lift.

What are the interestingness measures for association rules?

We present an experimental study of the behaviour of the interestingness measures such as lift, rule interest, Laplace, and information gain. Our experimental results verify that many of these measures are very similar in nature. From the findings, we introduce a classification of the current interestingness measures.

What are the metrics for association mining?

Metrics for Association Rules. Minimum support and confidence are used to influence the build of an association model. Support and confidence are also the primary metrics for evaluating the quality of the rules generated by the model. Additionally, Oracle Data Mining supports lift for association rules.

How do you read association rules?

First, generally on interpretation of association rules. Assuming that 0.3 is support and 0.7 confidence, then the rule is to be read as variable 18x with value 0 (i.e. item 18×0) is with 70% probability associated with item trt1. In other words, 70% of transactions containing item 18×0 also contain item trt1.

What are two measures of association rule mining?

Measures of the effectiveness of association rules The strength of a given association rule is measured by two main parameters: support and confidence. Support refers to how often a given rule appears in the database being mined. Confidence refers to the amount of times a given rule turns out to be true in practice.

What is applicability of association rules?

Use cases for association rules In data science, association rules are used to find correlations and co-occurrences between data sets. They are ideally used to explain patterns in data from seemingly independent information repositories, such as relational databases and transactional databases.

What techniques can be used to improve the efficiency of Apriori algorithm?

Explanation: From the following options, all of the above i.e., hash – based techniques, transaction reduction and partitioning are the techniques that can be used to improve the efficiency of apriori algorithm.

What is an association rule give example?

Association Rule – An implication expression of the form X -> Y, where X and Y are any 2 itemsets. Example: {Milk, Diaper}->{Beer}

How are frequent itemsets and closed itemsets related?

Association rule mining: Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories. · A set of items is referred to as an itemset. · An itemset that contains k items is a k-itemset.

What are the association rules for frequent itemsets?

· The occurrence frequency of an itemset is the number of transactions that contain the itemset. · This is also known, simply, as the frequency, support count, or count of the itemset. Rules that satisfy both a minimum support threshold (min sup) and a minimum confidence threshold (min conf) are called Strong Association Rules.

How is association rule used in data mining?

Association rule mining finds interesting associations and relationships among large sets of data items. This rule shows how frequently a itemset occurs in a transaction.

When does an itemet become a maximal itemet?

If X is A union B then it is the number of transactions in which A and B both are present. Maximal Itemset: An itemset is maximal frequent if none of its supersets are frequent. Closed Itemset: An itemset is closed if none of its immediate supersets have same support count same as Itemset.