Data Mining: Association Mining

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Questions and Answers

What is the primary concept behind association mining?

The primary concept behind association mining is the identification of frequent patterns, correlations, and associations among sets of objects in databases.

Explain the roles of support, confidence, and lift in association rule mining.

Support measures the frequency of an itemset, confidence indicates the likelihood that the consequent occurs with the antecedent, and lift measures the strength of the association between the items.

Provide an example of an association rule and identify its antecedent and consequent.

An example of an association rule is 'If a customer buys a dozen eggs, then they are likely to also purchase milk,' where 'a dozen eggs' is the antecedent and 'milk' is the consequent.

How did Walmart utilize association mining to enhance sales?

<p>Walmart placed beer next to diapers based on the association that customers who buy diapers also tend to purchase beer, increasing the sales of both items.</p> Signup and view all the answers

Who are the scientists credited with formulating association rule mining?

<p>Dr. Rakesh Agrawal and Dr. Ramakrishnan Srikant are credited with formulating association rule mining.</p> Signup and view all the answers

What is the purpose of item association rule mining in a retail context?

<p>The purpose is to identify relationships between items that tend to be purchased together in order to enhance marketing strategies and improve customer satisfaction.</p> Signup and view all the answers

How is support calculated for an item such as Bread?

<p>Support is calculated by dividing the number of transactions containing Bread by the total number of transactions.</p> Signup and view all the answers

Explain why low support for an association rule may be problematic.

<p>Low support may indicate that the association occurs by chance, making it illogical to promote items that rarely sell together.</p> Signup and view all the answers

Define confidence in the context of association rules.

<p>Confidence measures the likelihood that the consequent item appears in a transaction given that the antecedent item is present.</p> Signup and view all the answers

Why is lift an important metric in item association rule mining?

<p>Lift evaluates how much more likely two items are to be bought together compared to being bought independently, offering deeper insights into product associations.</p> Signup and view all the answers

Calculate the confidence if there are 30 transactions containing Bread and 24 of those also contain Milk.

<p>The confidence of the rule (Bread -&gt; Milk) is $80%$.</p> Signup and view all the answers

What does a lift value greater than 1 indicate?

<p>A lift value greater than 1 indicates a positive association between the items, meaning they are more likely to be bought together than independently.</p> Signup and view all the answers

How can market basket analysis benefit retailers?

<p>Market basket analysis helps retailers discover purchase patterns to optimize product placements, enhance marketing strategies, and improve inventory management.</p> Signup and view all the answers

Flashcards

Association Mining

The process of finding relationships or patterns between items in datasets.

Support

A metric to determine the frequency of an itemset in a dataset. A higher support value means an itemset is more common.

Confidence

A measure of the likelihood of an itemset occurring together. A higher confidence means the rule is stronger.

Lift

A measure of the strength of an association rule, considering how much more likely the consequent is to occur when the antecedent occurs.

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Association Rule

A statement that predicts the occurrence of one item based on the occurrence of another item. It is written as "If X then Y", where X is the antecedent and Y is the consequent.

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Number of Items (n)

The number of items in a store's inventory.

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Item List (I)

A list of all the items available in a store, represented as {i1, i2, ..., in}.

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Number of Transactions (N)

The total number of transactions recorded in a dataset.

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Transaction (T)

A single purchase made by a customer, represented by T {t1, t2, ..., tN}.

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Market Basket Analysis

Analyzing customer purchase patterns to identify relationships between products bought together. E.g., Customers who buy bread are more likely to also buy milk.

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Study Notes

Data Mining: Association Mining

  • Association mining identifies frequent patterns, correlations, associations, or causal structures in data.
  • It often analyzes transactional databases, relational databases, and other information repositories.
  • A typical association mining rule is an if/then statement. For example, "If a customer buys a dozen eggs, they are 80% likely to also buy milk."
  • An association rule consists of two parts:
    • Antecedent: the object or item found in the data.
    • Consequent: the object or item found in combination with the antecedent.
  • Rules are written as X → Y, where X is the antecedent and Y is the consequent. This indicates that whenever X appears, Y also tends to appear.
  • Association rules were formulated by Dr. Rakesh Agrawal and Dr. R. Srikant.

Learning Objectives

  • Students will understand the concept of association mining and its various applications.
  • Students will learn the significance of support, confidence, and lift in evaluating association rules.

Introduction to Association Mining

  • Association mining is used to discover relationships among data items. The examples in the lecture illustrate common items purchased together.
  • For instance, customers buying diapers usually buy beer.
  • The placement of these products in stores is often a result of identifying those items that are commonly purchased together.

Defining Association Rules

  • Association rules are defined as identifying frequent patterns, correlations, associations, or causal structures in sets of objects in transactional, relational databases, and other information repositories.

Item Association Rule Mining Representation

  • A certain number of products (n) are considered in stock.
  • Each transaction is given a unique identifier (TID).
  • A transaction may include several items (e.g., Bread, Milk, Diapers, Beer, Eggs, Cola).
  • Examples of transactions are given (e.g., {Bread, Milk}, {Bread, Diapers, Beer, Eggs}).

Metrics to Evaluate Associations

  • Support: Measures how frequently two items occur together in the dataset, calculated by dividing the number of transactions containing both items by the total number of transactions.
  • Confidence: Measures how likely item Y will appear given that item X is present in a transaction. It is computed by dividing the support of X and Y by the support of X.
  • Lift: Measures the strength of an association rule relative to random occurrence. It is calculated by dividing the confidence of the rule by the support of Y.

Support

  • Support(X) = Number of times X appears / Total number of transactions
  • Support(X, Y) = Number of times X and Y appear together / Total number of transactions

Confidence

  • Confidence(X → Y) = Support(X, Y) / Support(X)

Lift

  • Lift(X → Y) = Confidence(X → Y) / Support(Y)

Application of Association Rules

  • Market Basket Analysis: Identify frequently bought-together products.
  • Recommender Systems: Propose products tailored to user preferences.
  • Customer Segmentation: Group customers with similar purchasing habits.
  • Supply Chain Management: Optimize inventory control based on demand patterns.
  • Telecommunications: Predict customer churn based on behavior patterns.
  • Travel and Tourism: Recommend travel packages based on common customer preferences.

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Data Mining Association Mining - BPD2233 - PDF

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