1. Data Mining Chapter 2 Association Rule Mining
G. K. Gupta
Faculty of Information Technology
Monash University

2. Introduction
As noted earlier, huge amount of data is stored electronically in many retail outlets due to barcoding of goods sold. Natural to try to find some useful information from this mountains of data.
A conceptually simple yet interesting technique is to find association rules from these large databases.
The problem was invented by Rakesh Agarwal at IBM.
Basket analysis is useful in determining what products customers are likely to purchase together. The analysis can also be useful in determining what purchases are made together over a period of time.
An example might be that a person who has bought a lounge suit is likely to buy a TV next.
This type of analysis is useful in marketing (e.g. coupning and advertising), store layout etc.
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3. Introduction
Association rules mining (or market basket analysis) searches for interesting customer habits by looking at associations.
The classical example is the one where a store in USA was reported to have discovered that people buying nappies tend also to buy beer.
Applications in marketing, store layout, customer segmentation, medicine, finance, and many more.
Basket analysis is useful in determining what products customers are likely to purchase together. The analysis can also be useful in determining what purchases are made together over a period of time.
An example might be that a person who has bought a lounge suit is likely to buy a TV next.
This type of analysis is useful in marketing (e.g. coupning and advertising), store layout etc.
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4. A Simple Example Consider the following ten transactions of a shop selling only nine items. We will return to it after explaining some terminology.
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5. Terminology Let the number of different items sold be n
Let the number of transactions be N
Let the set of items be {i1, i2, …, in}. The number of items may be large, perhaps several thousands.
Let the set of transactions be {t1, t2, …, tN}. Each transaction ti contains a subset of items from the itemset {i1, i2, …, in}. These are the things a customer buys when they visit the supermarket. N is assumed to be large, perhaps in millions.
Not considering the quantities of items bought.
Basket analysis is useful in determining what products customers are likely to purchase together. The analysis can also be useful in determining what purchases are made together over a period of time.
An example might be that a person who has bought a lounge suit is likely to buy a TV next.
This type of analysis is useful in marketing (e.g. coupning and advertising), store layout etc.
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6. Terminology
Want to find a group of items that tend to occur together frequently.
The association rules are often written as X→Y meaning that whenever X appears Y also tends to appear. X and Y may be single items or sets of items but the same item does not appear in both.
Basket analysis is useful in determining what products customers are likely to purchase together. The analysis can also be useful in determining what purchases are made together over a period of time.
An example might be that a person who has bought a lounge suit is likely to buy a TV next.
This type of analysis is useful in marketing (e.g. coupning and advertising), store layout etc.
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7. Terminology
Suppose X and Y appear together in only 1% of the transactions but whenever X appears there is 80% chance that Y also appears.
The 1% presence of X and Y together is called the support (or prevalence) of the rule and 80% is called the confidence (or predictability) of the rule.
These are measures of interestingness of the rule.
Confidence denotes the strength of the association between X and Y. Support indicates the frequency of the pattern. A minimum support is necessary if an association is going to be of some business value.
It is important to define the concept of lift which is commonly used in mail-order marketing.
Suppose 5% of customers to a store buy TV but customers who buy a lounge suite are much more likely to buy a TV. Let that likelihood be 20%. The ratio of 20% to 5% (i.e. 4) is called lift. Lift essentially measures the strength of the relationship X=>Y.
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8. Terminology
Let the chance of finding an item X in the N transactions is x% then we can say probability of X is P(X) = x/100 since probability values are always between 0.0 and 1.0.
Now suppose we have two items X and Y with probabilities of P(X) = 0.2 and P(Y) = 0.1. What does the product P(X) x P(Y) mean?
What is likely to be the chance of both items X and Y appearing together, that is P(X and Y) = P(X U Y)?
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9. Terminology
Suppose we know P(X U Y) then what is the probability of Y appearing if we know that X already exists. It is written as P(Y|X)
The support for X→Y is the probability of both X and Y appearing together, that is P(X U Y).
The confidence of X→Y is the conditional probability of Y appearing given that X exists. It is written as P(Y|X) and read as P of Y given X.
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10. Terminology
Sometime the term lift is also used. Lift is defined as Support(X U Y)/P(X)P(Y). P(X)P(Y) is the probability of X and Y appearing together if both X and Y appear randomly.
As an example, if support of X and Y is 1%, and X appears 4% in the transactions while Y appears in 2%, then lift = 0.01/0.04x0.02 = What does it tell us about X and Y? What if the lift was 1?
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11. The task
Want to find all associations which have at least p% support with at least q% confidence such that
all rules satisfying any user constraints are found
the rules are found efficiently from large databases
the rules found are actionable
As an example, let us consider that a furniture and appliances store has 100,000 records of sale.
Let 5,000 records contain lounge suites (5%),
Let 10,000 records contain TVs (10%).
It is expected that the 5,000 records containing lounge suites will contain 10% TVs (i.e. 500). If the number of TV sales in the 5,000 records is in fact 2,000 then we have a lift of 4 or confidence of 40%.
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12. An Example
Consider a furniture and appliances store has 100,000 records of sale. Let 5,000 records contain lounge suites (5%), and let 10,000 records contain TVs (10%). The support for lounge suites is therefore 5% and for TVs 10%.
It is expected that the 5,000 records containing lounge suites will contain 10% TVs (i.e. 500). If the number of TV sales in the 5,000 records is in fact 2,500 then we have a lift of 5 or confidence of the rule Lounge→TV is 50%.
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13. Question
With 100,000 records, 5,000 records containing lounge suites (5%), and 10,000 containing TVs (10%) and the number of TV sales in the 5,000 records containing lounge suite being 2,500, what is the support and the confidence in the following two rules?
lounge → TV
and TV → lounge
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14. Associations It is worth noting:
The lift of X → Y is the same as that of Y → X (why?). Would the confidence be the same?
The only rules of interest are with very high or very low lift (why?)
Items that appear on most transactions are of little interest (why?)
Similar items should be combined to reduce the number of total items (why?)
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15. Applications
Although we are only considering basket analysis, the technique has many other applications as noted earlier.
For example we may have many patients coming to a hospital with various diseases and from various backgrounds. We therefore have a number of attributes (items) and we may be interested in knowing which attributes appear together and may be which attributes are frequently associated to some particular disease.
Associations can also determine rules like "A customer buying shares in Microsoft is also likely to buy shares in AOL".
X=>Y type rules have a left-hand side (called antecedent) and a right-hand side called (consequent). It is assumed that X and Y have no common items.
Associations could also be considered as a type of clustering
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16. Example We have 9 items and 10 transactions
Example We have 9 items and 10 transactions. Find support and confidence for each pair. How many pairs are there?
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17. Example
There are 9 x 8 or 72 pairs, half of them duplicates. 36 is too many to analyse in a class, so we use an even simpler example of n = 4 (Bread, Cheese, Juice, Milk) and N = 4. We want to find rules with minimum support of 50% and minimum confidence of 75%.
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18. Example N = 4 results in the following frequencies. All items have
the 50% support we
require. Only two pairs
have the 50% support
and no 3-itemset has
the support.
We will look at the
two pairs that have
the minimum support
to find if they have
the required confidence.
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19. Terminology
Items or itemsets that have the minimum support are called frequent. In our example, all the four items and two pairs are frequent.
We will now determine if the two pairs {Bread, Cheese} and {Cheese, Juice} lead to association rules with 75% confidence.
Every pair {A, B} can lead to two rules A → B and B → A if both satisfy the minimum confidence. Confidence of A → B is given by the support for A and B together divided by the support of A.
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20. Rules
We have four possible rules and their confidence is given as follows:
Bread  Cheese with confidence of 2/3 = 67%
Cheese  Bread with confidence of 2/3 = 67%
Cheese  Juice with confidence of 2/3 = 67%
Juice  Cheese with confidence of 100%
Therefore only the last rule Juice  Cheese has confidence above the minimum 75% and qualifies. Rules that have more than user-specified minimum confidence are called confident.
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21. Problems with Brute Force
This simple algorithm works well with four items since there were only a total of 16 combinations that we needed to look at but if the number of items is say 100, the number of combinations is much larger, in billions.
The number of combinations becomes about a million with 20 items since the number of combinations is 2n with n items (why?). The naïve algorithm can be improved to deal more effectively with larger data sets.
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22. Problems with Brute Force
Can you think of an improvement over the brute force method in which we looked at every itemset combination possible?
Naïve algorithms even with improvements don’t work efficiently enough to deal with large number of items and transactions. We now define a better algorithm.
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23. The Apriori Algorithm
To find associations, this classical Apriori algorithm may be simply described by a two step approach:
Step 1 ─ discover all frequent (single) items that have support above the minimum support required
Step 2 ─ use the set of frequent items to generate the association rules that have high enough confidence level
Is this a reasonable algorithm?
A more formal description is given on the next slide.
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24. The Apriori Algorithm
The step-by-step algorithm may be described as follows
Computing L1
This is Step 1. Scan all transactions. Find all frequent items that have support above the required p%. Let all of these frequent items be labeled L1.
Apriori-gen Function
This is Step 2. Use the frequent items L1 to build all possible item pairs like {Bread, Cheese} if Bread and Cheese are in L1. The set of these item pairs is called C2, the candidate set.
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25. The Apriori Algorithm Pruning
This is Step 3. Scan all transactions and find all frequent pairs in the candidate pair setC2. Let these frequent pairs be L2.
General rule
This is Step 4, a generalization of Step 2. Build candidate set of k items Ck by combining frequent itemsets in the set Lk-1.
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26. The Apriori Algorithm Pruning
Step 5, generalization of Step 3. Scan all transactions and find all frequent item sets in Ck. Let these frequent itemsets be Lk.
Continue with Step 4 unless Lk is empty.
Stop if Lk is empty
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27. An Example
This example is similar to the last example but we have added two more items and another transaction making five transactions and six items. We want to find association rules with 50% support and 75% confidence.
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28. Example
First find L1. 50% support requires that each frequent item appear in at least three transactions. Therefore L1 is given by:
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29. Example
The candidate 2-itemsets or C2 therefore has six pairs (why?). These pairs and their frequencies are:
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30. Deriving Rules
L2 has only two frequent item pairs {Bread, Juice} and {Cheese, Juice}. After these two frequent pairs, there are no candidate 3-itemsets (why?) since we do not have two 2-itemsets that have the same first item (why?).
The two frequent pairs lead to the following possible rules:
Bread → Juice
Juice → Bread
Cheese → Juice
Juice → Cheese
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31. Deriving Rules
The confidence of these rules is obtained by dividing the support for both items in the rule by the support of the item on the left hand side of the rule.
The confidence of the four rules therefore are
3/4 = 75% (Bread → Juice)
3/4 = 75% (Juice → Bread)
3/3 = 100% (Cheese → Juice)
3/4 = 75% (Juice → Cheese)
Since all of them have a minimum 75% confidence, they all qualify. We are done since there are no 3– itemsets.
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32. Questions How do we compute C3 from L2 or more generally Ci from Li-1?
If there were several members of the set C3, how do we derive L3 by pruning C3?
Assume that the 3-itemset {A, B, M} has the minimum support. What do we do next? How do we derive association rules?
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33. Answers
To compute C3 from L2, or more generally Ci from Li-1, we join members of Li-1 to other members in Li-1 by first sorting the itemsets in their lexicographic order and then joining those itemsets in which the first (i-2) items are common.
Observe that if an itemset in C3 is (a, b, c) then L2 must have had itemsets (a, b) and (a, c) since all subsets of C3 must be frequent. Why?
To repeat, itemsets in a candidate set Ci or a frequent set Li will be frequent only if every subset is frequent.
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34. Answers
For example, {A, B, M} will be frequent only if {A, B}, {A,M} and {B, M} are frequent, which in turn requires each of A, B and M also to be frequent.
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35. Example
If the set {A, B, M} has the minimum support, we can find association rules that satisfy the conditions of support and confidence by first generating all nonempty subsets of {A, B, M} and using each of it on the LHS and remaining symbols on the RHS.
Subsets of {A, B, M} are A, B, M, AB, AM, BM. Therefore possible rules involving all three items are A → BM, B → AM, M → AB, AB → M, AM → B and BM → A.
Now we need to test their confidence.
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36. Answers
To test the confidence of the possible rules, we proceed as we have done before. We know that
confidence(A → B) = P(B|A) = P(A U B)/ P(A)
This confidence is the likelihood of finding B if A already has been found in a transaction. It is the ratio of the support for A and B together and support for A by itself.
The confidence of all these rules can thus be computed.
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37. Efficiency
Consider an example of a supermarket database which might have several thousand items including 1000 frequent items and several million transactions.
Which part of the apriori algorithm will be most expensive to compute? Why?
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38. Efficiency
The algorithm to construct the candidate set Ci to find the frequent set Li is crucial to the performance of the Apriori algorithm. The larger the candidate set, higher the processing cost of discovering the frequent itemsets since the transactions must be scanned for each. Given that the numbers of early candidate itemsets are very large, the initial iterations dominate the cost.
In a supermarket database with about 1000 frequent items, there will be almost half a million candidate pairs C2 that need to be searched for to find L2.
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39. Efficiency
Generally the number of frequent pairs out of half a million pairs will be small and therefore (why?) the number of 3-itemsets should be small.
Therefore, it is the generation of the frequent set L2 that is the key to improving the performance of the Apriori algorithm.
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40. Comment
In class examples we usually require high support, for example, 25%, 30% or even 50%. These support values are very high if the number of items and number of transactions is large. For example, 25% support in a supermarket transaction database means searching for items that have been purchased by one in four customers! Not many items would probably qualify.
Practical applications therefore deal with much smaller support, sometimes even down to 1% or lower.
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41. Improving the Apriori Algorithm
Many techniques for improving the efficiency have been proposed:
Pruning (already mentioned)
Hashing based technique
Transaction reduction
Partitioning
Sampling
Dynamic itemset counting
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42. Pruning
Pruning can reduce the size of the candidate set Ck. We want to transform Ck into a set of frequent items Lk. To reduce the work of checking, we may use the rule that all subsets of Ck must also be frequent.
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43. Example Suppose the items are A, B, C, D, E, F, .., X, Y, Z
Suppose L1 is A, C, E, P, Q, S, T, V, W, X
Suppose L2 is {A, C}, {A, F}, {A, P}, {C, P}, {E, P},
{E, G}, {E, V}, {H, J}, {K, M}, {Q, S}, {Q, X}
Are you able to identify errors in the L2 list?
What is C3?
How to prune C3?
C3 is {A, C, P}, {E, P, V}, {Q, S, X}
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44. Hashing
The direct hashing and pruning (DHP) algorithm attempts to generate large itemsets efficiently and reduces the transaction database size.
When generating L1, we also generate all the 2-itemsets for each transaction, hash them in a hash table and keep a count.
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45. Example 1 2 5 7 3 4 6 11 13 14 19 12 {1,2} --> hash address 2
How does hashing work for this example? Let us look at possible 2-itemsets from the first transaction.
{1,2} --> hash address 2
{1,5} --> 5
{1,7} --> 7
{2,5} --> 2
{2,7} --> 6
{5,7} --> 3
We have used a simple hashing function which involves multiplying the two item numbers and then mod 8. Note there are collisions. 1 2 5 7 3 4 6 11 13 14 19 12
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46. Transaction Reduction
As discussed earlier, any transaction that does not contain any frequent k-itemsets cannot contain any frequent (k+1)-itemsets and such a transaction may be marked or removed.
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47. Example
Frequent items (L1) are A, B, D, M, T. We are not able to use these to eliminate any transactions since all transactions have at least one of the items in L1. The frequent pairs (C2) are {A,B} and {B,M}. How can we reduce transactions using these?
TID
Items bought
001
B, M, T, Y
002
B, M
003
T, S, P
004
A, B, C, D
005
A, B
006
T, Y, E
007
A, B, M
008
B, C, D, T, P
009
D, T, S
010
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48. Partitioning
The set of transactions may be divided into a number of disjoint subsets. Then each partition is searched for frequent itemsets. These frequent itemsets are called local frequent itemsets.
How can information about local itemsets be used in finding frequent itemsets of the global set of transactions?
In the example on the next slide, we have divided a set of transactions into two partitions. Find the frequent items sets for each partition. Are these local frequent itemsets useful?
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49. Example 1 2 5 7 3 4 6 11 13 14 19 12 2 4 6 11 13 5 7 1 3
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50. Partitioning Phase 1 Divide n transactions into m partitions
Find the frequent itemsets in each partition
Combine all local frequent itemsets to form candidate itemsets
Phase 2
Find global frequent itemsets
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51. Sampling
A random sample (usually large enough to fit in the main memory) may be obtained from the overall set of transactions and the sample is searched for frequent itemsets. These frequent itemsets are called sample frequent itemsets.
How can information about sample itemsets be used in finding frequent itemsets of the global set of transactions?
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52. Sampling
Not guaranteed to be accurate but we sacrifice accuracy for efficiency. A lower support threshold may be used for the sample to ensure not missing any frequent datasets.
The actual frequencies of the sample frequent itemsets are then obtained.
More than one sample could be used to improve accuracy.
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53. Problems with Association Rules Algorithms
Users are overwhelmed by the number of rules identified ─ how can the number of rules be reduced to those that are relevant to the user needs?
Apriori algorithm assumes sparsity since number of items on each record is quite small.
Some applications produce dense data which may also have
many frequently occurring items
strong correlations
many items on each record
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54. Problems with Association Rules
Also consider:
AB → C (90% confidence)
and A → C (92% confidence)
Clearly the first rule is of no use. We should look for more complex rules only if they are better than simple rules.
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55. Bibliography
R. Agarwal, T. Imielinski, and A. Swami, Mining Association Rules Between sets of Items in Large Databases, In Proc of the ACM SIGMOD, 1993, pp
R. Ramakrishnan and J. Gehrke, Database management systems,, 2nd ed. McGraw-Hill, 2000.
M. J. A. Berry and G. Linoff, Mastering data mining : the art and science of customer relationship management, Wiley, 2000.
I. H. Witten and E. Frank, Data mining: practical machine learning tools and techniques with Java implementations,. Morgan Kaufmann, 2000.
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56. Bibliography
M. J. A. Berry and G. Linoff, Data mining techniques: for marketing, sales, and customer support, New York : Wiley, 1997.
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.), Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996.
R. Agarwal, M. Mehta, J. Shafer, A. Arning, and T. Bollinger, The Quest Data Mining System, Proc 1996 Int. Conf on Data Mining and Knowledge Discovery (KDD’96), Portland, Oregon, pp , Aug 1996.
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