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Presentation Transcript

Evgueni Smirnov

- Association Rule Problem
- Applications
- The Apriori Algorithm
- Discovering Association Rules
- Techniques to Improve Efficiency of Association Rule Mining
- Measures for Association Rules

Applications

- Market Basket Analysis: given a database of customer transactions, where each transaction is a set of items the goal is to find groups of items which are frequently purchased together.
- Telecommunication (each customer is a transaction containing the set of phone calls)
- Credit Cards/ Banking Services (each card/account is a transaction containing the set of customer’s payments)
- Medical Treatments (each patient is represented as a transaction containing the ordered set of diseases)
- Basketball-Game Analysis (each game is represented as a transaction containing the ordered set of ball passes)

- I={i1, i2, ..., in}: a set of all the items
- Transaction T: a set of items such that TI
- Transaction DatabaseD: a set of transactions
- A transaction TIcontains a set XI of some items, if XT
- An Association Rule: is an implication of the form XY, where X, YI

- A set of items is referred as an itemset. A itemset that contains k items is a k-itemset.
- The support s of an itemset Xis the percentage of transactions in the transaction databaseDthat contain X.
- The support of the rule XY in the transaction databaseDis the support of the items set XYin D.
- The confidence of the rule XY in the transaction databaseDis the ratio of the number of transactions in D that contain XY to the number of transactions that contain X in D.

- Given:
- a set I of all the items;
- a database D of transactions;
- minimum support s;
- minimum confidence c;
- Find:
- all association rules XY with a minimum support s and confidence c.

1. Find all sets of items that have minimum support (frequent itemsets)

2. Use the frequent itemsets to generate the desired rules

If the minimum support is 50%, then {Shoes,Jacket} is the only 2- itemset that satisfies the minimum support.

If the minimum confidence is 50%, then the only two rules generated from this 2-itemset, that have confidence greater than 50%, are:

Shoes Jacket Support=50%, Confidence=66%

Jacket Shoes Support=50%, Confidence=100%

- Frequent Itemset Property:

Any subset of a frequent itemset is frequent.

- Contrapositive:

If an itemset is not frequent, none of its supersets are frequent.

- Lk: Set of frequent itemsets of size k (with min support)
- Ck: Set of candidate itemset of size k (potentially frequent itemsets)

L1 = {frequent items};

for (k = 1; Lk !=; k++) do

Ck+1 = candidates generated from Lk;

for each transaction t in database do

increment the count of all candidates in Ck+1 that are contained in t

Lk+1 = candidates in Ck+1 with min_support

returnkLk;

Input: Li-1:set of frequent itemsets of size i-1

Output: Ci :set of candidate itemsets of size i

Ci= empty set;

for each itemset J in Li-1do

for each itemset K in Li-1s.t. K<> J do

ifi-2 of the elements in J and K are equal then

if all subsets of {K J} are in Li-1then

Ci = Ci {K J}

return Ci;

Example of Generating Candidates

- L3={abc, abd, acd, ace, bcd}
- Generating C4 from L3
- abcd from abc and abd
- acde from acd and ace
- Pruning:
- acde is removed because ade is not in L3
- C4={abcd}

Let use consider the 3-itemset {I1, I2, I5}:

I1 I2 I5

I1 I5 I2

I2 I5 I1

I1 I2 I5

I2 I1 I5

I5 I1 I2

for each frequent itemset Ido

for each subset C of Ido

if (support(I) / support(I - C) >= minconf) then

output the rule (I - C) C,

with confidence = support(I) / support (I - C)

and support = support(I)

Let use consider the 3-itemset {I1, I2, I5} with support of 0.22(2)%. Let generate all the association rules from this itemset:

I1 I2 I5 confidence= 2/4 = 50%

I1 I5 I2 confidence= 2/2 = 100%

I2 I5 I1 confidence= 2/2 = 100%

I1 I2 I5 confidence= 2/6 = 33%

I2 I1 I5 confidence= 2/7 = 29%

I5 I1 I2 confidence= 2/2 = 100%

Apriori Advantages/Disadvantages

- Advantages:
- Uses large itemset property.
- Easily parallelized
- Easy to implement.
- Disadvantages:
- Assumes transaction database is memory resident.
- Requires many database scans.

A transaction that does not contain any frequent k-itemset will not contain frequent l-itemset for l >k! Thus, itis useless in subsequent scans!

Sampling

Mining on a subset of given data, lower support threshold + a method to determine the completeness

Alternative Measures for Association Rules

- The confidence of XYin database D is the ratio of the number of transactions containing XY to the number of transactions that contain X. In other words the confidence is:
- But, when Y is independent of X: p(Y) = p(Y | X). In this case if p(Y) is high we’ll have a rule with high confidence that associate independent itemsets! For example, if p(“buy milk”) = 80% and “buy milk” is independent from “buy salmon”, then the rule “buy salmon” “buy milk” will have confidence 80%!

The lift measure indicates the departure from independence of X and Y. The lift of XYis :

But, the lift measure is symmetric; i.e., it does not take into account the direction of implications!

Alternative Measures for Association RulesThe conviction measure indicates the departure from independence of X and Y taking into account the implication direction. The conviction of XYis :Alternative Measures for Association Rules

- Association Rules form an very applied data mining approach.
- Association Rules are derived from frequent itemsets.
- The Apriori algorithm is an efficient algorithm for finding all frequent itemsets.
- The Apriori algorithm implements level-wise search using frequent irem property.
- The Apriori algorithm can be additionally optimised.
- There are many measures for association rules.

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