Association Rule mining
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Association rule mining is an unsupervised learning technique used to discover relationships between variables in a large dataset. It analyzes how frequently items are purchased together and generates rules based on metrics like support, confidence and lift. For example, it can determine that customers who buy milk and diapers are likely to also purchase beer based on transaction histories. Association rule mining has applications in market basket analysis, medical diagnosis, catalog design and other domains.
Association Rule mining
- 1. Association Rule Mining OMega TechEd
- 2. Introduction 2 Association rule learning is a type of unsupervised learning technique that checks for the dependency of one data item on another data item and maps accordingly so that it can be more profitable. It tries to find some interesting relations or associations among the variables of dataset. It is based on different rules to discover the interesting relations between variables in the database. For example, if a customer buys bread, he most likely can also buy butter, eggs, or milk, so these products are stored within a shelf or mostly nearby. OMega TechEd
- 3. Applications Market Basket Analysis: It is one of the popular examples and applications of association rule mining. This technique is commonly used by big retailers to determine the association between items. Medical Diagnosis: With the help of association rules, patients can be cured easily, as it helps in identifying the probability of illness for a particular disease. Protein Sequence: The association rules help in determining the synthesis of artificial Proteins. It is also used for the Catalog Design and Loss-leader Analysis and many more other applications. 3 OMega TechEd
- 4. Working Association rule learning works on the concept of If and Else Statement, such as if A then B. Here the If element is called antecedent, and then statement is called as Consequent. These types of relationships where we can find out some association or relation between two items is known as single cardinality. It is all about creating rules, and if the number of items increases, then cardinality also increases accordingly. So, to measure the associations between thousands of data items, there are several metrics. • Support • Confidence • Lift 4 OMega TechEd
- 5. Support Support is the frequency of A or how frequently an item appears in the dataset. It is defined as the fraction of the transaction T that contains the itemset X. If there are X datasets, then for transactions T, It can be written as- 5 TID Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke {Milk, Diaper}{Beer} Support = σ ({Milk, Diaper, Beer}) / T = 2/5 = 0.4 OMega TechEd
- 6. Confidence Confidence indicates how often the rule has been found to be true. Or how often the items X and Y occur together in the dataset when the occurrence of X is already given. It is the ratio of the transaction that contains X and Y to the number of records that contain X. 6 TID Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke {Milk, Diaper}{Beer} Confidence = σ ({Milk, Diaper, Beer}) / σ ({Milk, Diaper}) = 2/3 = 0.67 OMega TechEd
- 7. Lift It is the strength of any rule, which can be defined as below formula: It is the ratio of the observed support measure and expected support if X and Y are independent of each other. It has three possible values: • If Lift= 1: The probability of occurrence of antecedent and consequent is independent of each other. • Lift>1: It determines the degree to which the two itemset are dependent to each other. • Lift<1: It tells us that one item is a substitute for other items, which means one item has a negative effect on another. 7 OMega TechEd
- 8. Example 8 TID Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke {Milk, Diaper}{Beer} Lift= Supp({Milk, Diaper, Beer}) / Supp({Milk, Diaper})*Supp({Beer}) Supp({Milk, Diaper, Beer})=2/5 = 0.4 Supp({Milk, Diaper}) = 3/5 = 0.6 Supp({Beer}) = 3/5 =0.6 0.4/(0.6*0.6) = 1.11 High Association OMega TechEd
- 9. Conclusion The Association rule is very useful in analyzing datasets. The data is collected using bar-code scanners in supermarkets. Such databases consists of many transaction records which list all items bought by a customer on a single purchase. So, the manager could know if certain groups of items are consistently purchased together and use this data for adjusting store layouts, cross-selling, promotions based on statistics. 9 OMega TechEd
- 10. Thank you Reference: Artificial Intelligence: A Modern Approach, 3rd ed. Stuart Russell and Peter Norvig https://www.javatpoint.com/reinforcement-learning Join Telegram channel for AI notes. Link is in the description. OMega TechEd