What is 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 interestingness measures?
Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to be reduced.
What are the measures of association rule?
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?
Let’s understand each of them:
- Support. Support is the frequency of A or how frequently an item appears in the dataset.
- Confidence. Confidence indicates how often the rule has been found to be true.
- Lift. It is the strength of any rule, which can be defined as below formula:
What are the two steps of association rule mining?
An association rule has two parts: an antecedent (if) and a consequent (then). An antecedent is an item found within the data. A consequent is an item found in combination with the antecedent.
What does FP growth algorithm do?
What is FP-Growth. FP-growth is an improved version of the Apriori Algorithm which is widely used for frequent pattern mining(AKA Association Rule Mining). The Apriori Algorithm produces frequent patterns by generating itemsets and discovering the most frequent itemset over a threshold “minimal support count”.
What is interestingness of a pattern?
An interesting pattern represents knowledge. These are based on the structure of discovered patterns and the statistics underlying them. An objective measure for association rules of the form X Y is rule support, representing the percentage of transactions from a transaction database that the given rule satisfies.
What is association rules coverage?
Coverage (also called cover or LHS-support) is the support of the left-hand-side of the rule, i.e., supp(X). It represents a measure of to how often the rule can be applied. Coverage is quickly calculated from the rules quality measures (support and confidence) stored in the quality slot.
What is association analysis?
Association analysis is the task of finding interesting relationships in large datasets. These interesting relationships can take two forms: frequent item sets or association rules. Frequent item sets are a collection of items that frequently occur together.
How do you mine association rules?
Steps involved in Association Rule Mining
- Step 1: Find all frequent itemsets. An itemset is a set of items that occurs in a shopping basket.
- Step 2: Generate strong association rules from the frequent itemsets. Association rules are generated by building associations from frequent itemsets generated in step 1.
What do you do with 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.