2023 Supervised_Learning_Association_Rules
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This document provides an overview of unsupervised learning techniques including k-means clustering and association rule mining. It begins with introductions to the speaker and tutorial topics. It then contrasts supervised vs unsupervised learning, describing how k-means is used for clustering without labels and how association rules can discover relationships between items. The document provides examples of applying these techniques in domains like retail, sports, email marketing and healthcare. It also includes visualizations and discusses important concepts for k-means like data transformation and for association rules like support, confidence and lift. Homework questions are asked about preparing data for these algorithms in Orange.
![Association Rules
• Supportance
• If the total transaction data has 200 records, and the item Sausage
has 20 records, then its Supportance is 50/200 = 1/4, that is, the
support of sausage is 25%.
• Confidence range: [0, 1].
• Indicates the conditional probability of two items appearing at the
same time. Simply put, it is the probability of item A appearing
when item B has already appeared.
29
Confidence(A -> B) =
• P（A|B）: The probability that A will occur
under the conditions that B occurs
• P ( A ∩ B ) or P ( A , B ) or P ( A B ) : The
probability that two events will occur together](https://image.slidesharecdn.com/2unsupervisedlearningassociationrules-231219054549-895b4313/85/2023-Supervised_Learning_Association_Rules-29-320.jpg)
2023 Supervised_Learning_Association_Rules
- 2. About me • Education • NCU (MIS)、NCCU (CS) • Work Experience • Telecom big data Innovation • AI projects • Retail marketing technology • User Group • TW Spark User Group • TW Hadoop User Group • Taiwan Data Engineer Association Director • Research • Big Data/ ML/ AIOT/ AI Columnist 2
- 3. Tutorial Content 3 Getting started unsupervised learning with Orange3 (K-means and Associated Rules) Home works What is the unsupervised learning
- 4. Supervised learning vs. Unsupervised learning • Supervised learning: Discover patterns in the data that relate data attributes with a target (class) attribute. • These patterns are then utilized to predict the values of the target attribute in future data instances. • Unsupervised learning: The data have no target attribute. • We want to explore the data to find some intrinsic structures in them. • Classic unsupervised learning algorithm • Clustering algorithms (Inductive/ Transductive learning) • Association rules (also called Market Basket Analysis) 4
- 6. K-means
- 7. K-means 7
- 8. K-means (Data observation: Shall we PREPROC data?) 8
- 10. Transform data before K-means • Many statistical tests make the assumption that datasets are normally distributed. • However, this is often NOT the case in practice. • Transformations: • Log Transformation: Transform the response variable from y to log(y). • Square Root Transformation: Transform the response variable from y to y1/2. • Cube Root Transformation: Transform the response variable from y to y1/3.
- 14. Quiz 1: • Why should we transform data? • Answer 1 : To avoid overfitting. Ok, but what is the overfitting?
- 15. Re-Scaling
- 16. Standardize Data • Standardization (Z-scores) rescales a dataset to have a mean of 0 and a standard deviation of 1. • We typically standardize data when we’d like to know how many standard deviations each value in a dataset lies from the mean.
- 17. Normalize Data • Normalization rescales a dataset so that each value falls between 0 and 1. • Typically we normalize data when performing some type of analysis in which we have multiple variables that are measured on different scales and we want each of the variables to have the same range.
- 18. Quiz 2: • When conducting K-means, how should categorical variable be handled? • When conducting K-means on numerical variables with severe skewness distribution, how to handle with it? • If we have segmented several groups by the re-scaled data, how to proceed new data and group assignment? (Using K-means) • Answer 1: Union all data, and rebuild model again. • Answer 2: ??
- 19. Example of Clustering analysis
- 20. Example of Cluster Analysis • Retail Marketing • The company can then send personalized advertisements or sales letters to each household based on how likely they are to respond to specific types of advertisements.
- 21. Example of Cluster Analysis • Streaming Services • Using these metrics, a streaming service can perform cluster analysis to identify high usage and low usage users so that they can know who they should spend most of their advertising dollars on.
- 22. Example of Cluster Analysis • Sports Science • They can then feed these variables into a clustering algorithm to identify players that are similar to each other so that they can have these players practice with each other and perform specific drills based on their strengths and weaknesses.
- 23. Example of Cluster Analysis • Email Marketing • Using these metrics, a business can perform cluster analysis to identify consumers who use email in similar ways and tailor the types of emails and frequency of emails they send to different clusters of customers. https://email.uplers.com/blog/email-segmentation-recipe-great-email-marketing/
- 24. Example of Cluster Analysis • Health Insurance • An actuary can then feed these variables into a clustering algorithm to identify households that are similar. The health insurance company can then set monthly premiums based on how often they expect households in specific clusters to use their insurance.
- 26. Association Rules • In a transaction database with a large amount of data, look for items correlations. • The classic story of Walmart diapers and beer. • Selling these two unrelated products together can actually increase sales. 26 In general, the correlations can’t be obtained through direct observation, but through algorithms.
- 27. Association Rules • Two steps as below. • First, obtain the frequent item sets! • A collection of items that often appear together. • Utilizing Apriori algorithm. • Second, generate Association Rules from frequent item sets! • There may be strong correlations based on frequent item sets. • Must meet the definition such like Min Supportance or Min confidence. 27
- 28. Association Rules • From sales database, we found {B, C, E} items have high correlation. That is called frequent item sets. • According to {B, E} are likely to be purchased together, that is called strength of association. • How strong of association, we estimate Supportance and Confidence. 28
- 29. Association Rules • Supportance • If the total transaction data has 200 records, and the item Sausage has 20 records, then its Supportance is 50/200 = 1/4, that is, the support of sausage is 25%. • Confidence range: [0, 1]. • Indicates the conditional probability of two items appearing at the same time. Simply put, it is the probability of item A appearing when item B has already appeared. 29 Confidence(A -> B) = • P（A|B）: The probability that A will occur under the conditions that B occurs • P ( A ∩ B ) or P ( A , B ) or P ( A B ) : The probability that two events will occur together
- 30. Association Rules • Min Supportance and Min Confidence: • Generally, we define support as 50%, which means that the purchased product set {A, B} appears in at least 50% of the total times before it is considered a frequent item set. 30 If the Supportance/ Confidence is set too low, too many association rules will appear in the results. If it is too high, there will be too few association rules, which is not conducive to us making decisions based on the association results. 。
- 31. Association Rules • Outputs: • A bunch of rules are generated, we use to sorting by Supportance or Confidence to find what we are interesting.
- 32. Example of Association Rules
- 33. Association Rules (Item Bundle sales ) • Mixed Bundling 33
- 34. Association Rules (Item Bundle sales ) • Cross industry bundling • Gund Teddy Bear and Amazon.com Gift Cards (Bundle)
- 37. Workflows
- 38. Have a look on Food dataset
- 39. Metrics Description Supportance how often a rule is applicable to a given data set (rule/data) Confidence how frequently items in Y appear in transactions with X or in other words how frequently the rule is true (support for a rule/support of antecedent) Coverage how often antecedent item is found in the data set (support of antecedent/data) Strength (support of consequent/support of antecedent) Lift how frequently a rule is true per consequent item (data * confidence/support of consequent) Leverage the difference between two item appearing in a transaction and the two items appearing independently (support*data - antecedent support * consequent support/data2)
- 42. Association Rules analysis A logical step would be to place Wine closer to the (Nuts, Aspirin, Pancakes) section The condition holds when looking from the left Antecedent toward on the right Consequent, but NOT in reverse!
- 43. Association Rules analysis • If we are running a promotion for Wine, which products should we emphasize?
- 44. Home works • Modifying the file format (20231121_hw.csv) to a format compatible with Orange 3 Association Rules. • Please identify what have you discovered any interesting association rules? The first row is all item names, go allover purchase item and mark the values 1; otherwise mark as ? (not 0)