Mining association rules between sets of items in large databases
1 / 11

Mining Association Rules between Sets of Items in Large Databases - PowerPoint PPT Presentation

  • Uploaded on

Mining Association Rules between Sets of Items in Large Databases. presented by Zhuang Wang. Outline. Introduction Formal Model Apriori Algorithm Experiments Summary. Introduction. Association rule:

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Mining Association Rules between Sets of Items in Large Databases' - albina

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Outline Databases

  • Introduction

  • Formal Model

  • Apriori Algorithm

  • Experiments

  • Summary

Introduction Databases

  • Association rule:

    - Association rules are used to discover elements that co-occur frequently within a dataset consisting of multiple independent selections of elements (such as purchasing transactions), and to discover rules.

  • Applications:

    - Questions such as "if a customer purchases product A, how likely is he to purchase product B?" and "What products will a customer buy if he buys products C and D?" are answered by association-finding algorithms.(market basket analysis)

Formal model
Formal Model Databases

  • Let I = I_1, I_2,. . ., I_n be a set of items.

    Let T be a database of transactions.

    Each transaction t in T is represented as a subset of I .

    Let X be a subset of I.

  • Support and Confidence:

    By an association rule, we mean an implication of the form X  I_k, where X is a set of some items in I, and I_k is a single item in I that is not present in X.

    support: probability that a transaction contains X and I_k.

    P(X ,I_k)

    confidence: conditional probability that a transaction having X also contains I_k.

    P(l_k | X)

Support and confidence example
Support and Confidence - Example Databases

  • Let minimum support 50%, and minimum confidence 50%, we have

    • A  C (50%, 66.6%)

    • C  A (50%, 100%)

Apriori algorithm
Apriori Algorithm Databases

  • To find subsets which are common to at least a minimum confidence of the itemsets.

  • Using a "bottom up" approach, where frequent itemsets (the sets of items that follows minimum support) are extended one item at a time (a step known as candidate generation), and groups of candidates are tested against the data.

  • The algorithm terminates when no further successful extensions are found.

  • Generating from each large itemset, rules that use items from the large itemset

Find frequent itemsets example
Find DatabasesFrequent Itemsets - Example

Database D



Scan D




Scan D



Scan D

Experiments Databases

  • We experimented with the rule mining algorithm using

    the sales data obtained from a large retailing company.

  • There are a total of 46,873 customer transactions in

    this data. Each transaction contains the department

    numbers from which a customer bought an item in

    a visit.

  • There are a total of 63 departments. The

    algorithm finds if there is an association between

    departments in the customer purchasing behavior.

  • The following rules were found for a minimum support of 1% and minimum condence of 50%.

  • [Tires]  [Automotive Services] (98.80, 5.79)

  • [Auto Accessories], [Tires]  [Automotive Services] (98.29, 1.47)

  • [Auto Accessories]  [Automotive Services] (79.51, 11.81)

  • [Automotive Services]  [Auto Accessories] (71.60, 11.81)

  • [Home Laundry Appliances]  [Maintenance Agreement Sales] (66.55, 1.25)

  • [Children's Hardlines]  [Infants and Children's wear] (66.15, 4.24)

  • [Men's Furnishing]  [Men's Sportswear] (54.86, 5.21)

Summary and minimum condence of 50%.

  • Apriori, while historically significant, suffers from a number of inefficiencies or trade-offs, which have spawned other algorithms.

  • Hash tables: uses a hash tree to store candidate itemsets. This hash tree has item sets at the leaves and at internal nodes

  • Partitioning: Any itemset that is potentially frequent in DB must be frequent in at least one of the partitions of DB

  • Sampling: mining on a subset of given data, need a lower support threshold + a method to determine the completeness.

Reference and minimum condence of 50%.

  • R. Agrawal, T. Imielinski, A. Swami: “Mining Associations between Sets of Items in Massive Databases”, Proc. of the ACM SIGMOD Int'l Conference on Management of Data, Washington D.C., May 1993, 207-216.