The document discusses association rule mining and the Apriori algorithm. It provides an overview of association rule mining, which aims to discover relationships between variables in large datasets. The Apriori algorithm is then explained as a popular algorithm for association rule mining that uses a bottom-up approach to generate frequent itemsets and association rules, starting from individual items and building up patterns by combining items. The key steps of Apriori involve generating candidate itemsets, counting their support from the dataset, and pruning unpromising candidates to create the frequent itemsets.
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A Market Basket Analysis of a bakery shop data using Apriori Algorithms and Association Rule mining . Application and Benefits of Market Basket Analytics in Retail Management
�
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5
Association Analysis:
Basic Concepts and
Algorithms
Many business enterprises accumulate large quantities of data from their day-
to-day operations. For example, huge amounts of customer purchase data are
collected daily at the checkout counters of grocery stores. Table 5.1 gives an
example of such data, commonly known as market basket transactions.
Each row in this table corresponds to a transaction, which contains a unique
identifier labeled TID and a set of items bought by a given customer. Retailers
are interested in analyzing the data to learn about the purchasing behavior
of their customers. Such valuable information can be used to support a vari-
ety of business-related applications such as marketing promotions, inventory
management, and customer relationship management.
This chapter presents a methodology known as association analysis,
which is useful for discovering interesting relationships hidden in large data
sets. The uncovered relationships can be represented in the form of sets of
items present in many transactions, which are known as frequent itemsets,
Table 5.1. An example of market basket transactions.
TID Items
1 {Bread, Milk}
2 {Bread, Diapers, Beer, Eggs}
3 {Milk, Diapers, Beer, Cola}
4 {Bread, Milk, Diapers, Beer}
5 {Bread, Milk, Diapers, Cola}
�
� �
�
358 Chapter 5 Association Analysis
or association rules, that represent relationships between two itemsets. For
example, the following rule can be extracted from the data set shown in
Table 5.1:
{Diapers} −→ {Beer}.
The rule suggests a relationship between the sale of diapers and beer because
many customers who buy diapers also buy beer. Retailers can use these types
of rules to help them identify new opportunities for cross-selling their products
to the customers.
Besides market basket data, association analysis is also applicable to data
from other application domains such as bioinformatics, medical diagnosis, web
mining, and scientific data analysis. In the analysis of Earth science data, for
example, association patterns may reveal interesting connections among the
ocean, land, and atmospheric processes. Such information may help Earth
scientists develop a better understanding of how the different elements of the
Earth system interact with each other. Even though the techniques presented
here are generally applicable to a wider variety of data sets, for illustrative
purposes, our discussion will focus mainly on market basket data.
There are two key issues that need to be addressed when applying associ-
ation analysis to market basket data. First, discovering patterns from a large
transaction data set can be computationally expensive. Second, some of the
discovered patterns may be spurious (happen simply by chance) and even for
non-spurious patterns, some are more interesting than others. The remainder
of this chapter is organized around these two issues. The first part of the
chapter is devoted to explaining the basic concepts of association ...
Products Frequently Bought Together in Stores Using classificat...hibaziyad99
Market Based Analysis is one of the key techniques used by large relations to show associations between items.
it can generate association rules from the given transactional datasets.Association rules are useful for analyzing and predicting customer behavior.
The disadvantage of association algorithms is require much more time to run than a decision tree algorithm.
The A priori Algorithm is an instrumental algorithm for mining familiar item sets.
The disadvantage is more exploration space and computational cost is too expensive.
Data Mining For Supermarket Sale Analysis Using Association Ruleijtsrd
Data mining is the novel technology of discovering the important information from the data repository which is widely used in almost all fields Recently, mining of databases is very essential because of growing amount of data due to its wide applicability in retail industries in improving marketing strategies. Analysis of past transaction data can provide very valuable information on customer behavior and business decisions. The amount of data stored grows twice as fast as the speed of the fastest processor available to analyze it.Its main purpose is to find the association relationship among the large number of database items. It is used to describe the patterns of customers purchase in the supermarket. This is presented in this paper. Rajeshri Shelke"Data Mining For Supermarket Sale Analysis Using Association Rule" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd94.pdf http://www.ijtsrd.com/engineering/computer-engineering/94/data-mining-for-supermarket-sale-analysis-using-association-rule/rajeshri-shelke
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1. ASSOCIATION RULE MINING
AND APRIORI ALGORITHM
By,
AMBOOJ
ANAM IQBAL
HINA FIRDAUS
M.Tech CSE 3rd Semester
Guided By,
Dr. Harleen Kaur
2. First proposed by Agrawal, Imielinski, and Swami frequent itemsets and
association rule mining
Motivation: Finding inherent regularities in data.
Pattern mining algorithms can be applied on various types of data such as :
transaction databases
sequence databases
stream, graph etc.
Pattern mining algorithms can be designed to discover various types of patterns:
subgraphs,
associations,
indirect associations,
trends,
periodic patterns,
sequential rules, lattices, sequential patterns, high-utility patterns, etc.
WHAT IS FREQUENT PATTERN ANALYSIS?
3. WHY IS FREQUENT PATTERN MINING IMPORTANT?
Frequent pattern:
An intrinsic and important property of datasets.
Foundation for many essential data mining tasks
Association, correlation, and causality analysis
Sequential, structural (e.g., sub-graph) patterns
Pattern analysis in spatiotemporal, multimedia, time-series, and stream data
Classification: discriminative, frequent pattern analysis
Cluster analysis: frequent pattern-based clustering
Data warehousing: iceberg cube and cube-gradient
Semantic data compression: fascicles
Broad applications
4. WHAT IS ASSOCIATION MINING?
Proposed by Agrawal et al in 1993.
Association rule mining
it is a procedure which is meant to find frequent patterns, correlations, associations, or causal
structures from data sets found in various kinds of databases such as relational databases,
transactional databases, and other forms of data repositories.
It is an important data mining model studied extensively by the database and data
mining community.
5. APPLICATION OF ASSOCIATION
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
6. MARKET BASKET ANALYSIS
INPUT: list of purchases by purchaser
do not have names
identify purchase patterns
what items tend to be purchased together
obvious: steak-potatoes; beer-pretzels
what items are purchased sequentially
obvious: house-furniture; car-tires
what items tend to be purchased by season
7. CONTINUE…
Categorize customer purchase behavior
identify actionable information
purchase profiles
profitability of each purchase profile
use for marketing
layout or catalogs
select products for promotion
space allocation, product placement
Market Basket Benefits
selection of promotions, merchandising strategy
sensitive to price: Italian entrees, pizza, pies, Oriental entrees, orange juice
uncover consumer spending patterns
correlations: orange juice & waffles
joint promotional opportunities
10. LIMITATIONS
takes over 18 months to implement
market basket analysis only identifies hypotheses, which need to be tested
neural network, regression, decision tree analyses
measurement of impact needed
difficult to identify product groupings
complexity grows exponentially
11. BENEFITS
simple computations
can be undirected (don’t have to have hypotheses before
analysis)
different data forms can be analyzed
12. ASSOCIATION RULES
Wal-Mart customers who purchase Barbie dolls have a 60%
likelihood of also purchasing one of three types of candy bars
[Forbes, Sept 8, 1997]
Customers who purchase maintenance agreements are very likely
to purchase large appliances (author experience)
When a new hardware store opens, one of the most commonly sold
items is toilet bowl cleaners (author experience)
So what…
13. WHAT IS ASSOCIATION RULE MINING?
Association Analysis is used for discovering interesting relationships
hidden in large data sets.
Proposed by Agrawal et al in 1993.
It is an important data mining model studied extensively by the
database and data mining community.
Assume all data are categorical.
Initially used for Market Basket Analysis to find how items purchased
by customers are related.
14. Finding frequent patterns, associations, correlations, or causal
structures among sets of items in transaction databases, relational
databases, and other information repositories.
Association rules are if/then statements that help uncover
relationships between seemingly unrelated data in a relational
database or other information repository.
Association rules are widely used in various areas such as
telecommunication networks, market and risk management,
inventory control etc.
Programmers use association rules to build programs capable of
machine learning.
CONTINUED…
15. The following rule can be extracted
from the data set shown in table 1:
{Diapers} {Beer}
The rule suggests that a strong
relationship exists between the sale
of diapers and beer because many
customers who buy diapers also buy
beer.
Retailers can use this type of rules to help them identify new
opportunities for cross-selling their products to the customers.
Applications Basket data analysis, cross-marketing, catalog design,
loss-leader analysis, web log analysis, fraud detection
16. An association rule has two parts, an antecedent (if) and a consequent (then).
An antecedent is an item found in the data.
A consequent is an item that is found in combination with the antecedent.
Rule form: Antecedent → Consequence
„Given:
(1) database of transactions,
(2) each transaction is a list of items purchased by a customer in a visit.
Find: all rules that correlate the presence of one set of items ( itemset ) with that
of another set of items.
„E.g., 98% of people who purchase tires and auto accessories also get
automotive services done
17. THE MODEL: RULES
A transaction t contains X, a set of items (itemset) in I, if X t.
An association rule is an implication of the form:
X Y, where X, Y I, and X Y =
An itemset is a set of items.
E.g., X = {milk, bread, cereal} is an itemset.
A k-itemset is an itemset with k items.
E.g., {milk, bread, cereal} is a 3-itemset
18. ASSOCIATION RULES
Association rule types:
Actionable Rules – contain high-quality, actionable information
Trivial Rules – information already well-known by those familiar with
the business
Inexplicable Rules – no explanation and do not suggest action
Trivial and Inexplicable Rules occur most often
19. HOW GOOD IS AN ASSOCIATION RULE?
Customer Items Purchased
1 OJ, soda
2 Milk, OJ, window cleaner
3 OJ, detergent
4 OJ, detergent, soda
5 Window cleaner, soda
OJ Window
cleaner
Milk Soda Detergent
OJ 4 1 1 2 2
Window cleaner 1 2 1 1 0
Milk 1 1 1 0 0
Soda 2 1 0 3 1
Detergent 2 0 0 1 2
POS Transactions
Co-occurrence of
Products
20. HOW GOOD IS AN ASSOCIATION RULE?
OJ Window
cleaner
Milk Soda Detergent
OJ 4 1 1 2 2
Window cleaner 1 2 1 1 0
Milk 1 1 1 0 0
Soda 2 1 0 3 1
Detergent 2 0 0 1 2
Simple patterns:
1. OJ and soda are more likely purchased together than
any other two items
2. Detergent is never purchased with milk or window cleaner
3. Milk is never purchased with soda or detergent
21. HOW GOOD IS AN ASSOCIATION RULE?
What is the confidence for this rule:
If a customer purchases soda, then customer also purchases OJ
2 out of 3 soda purchases also include OJ, so 67%
What about the confidence of this rule reversed?
2 out of 4 OJ purchases also include soda, so 50%
Confidence = Ratio of the number of transactions with all the items to
the number of transactions with just the “if” items
Customer Items Purchased
1 OJ, soda
2 Milk, OJ, window cleaner
3 OJ, detergent
4 OJ, detergent, soda
5 Window cleaner, soda
POS Transactions
22. CREATING ASSOCIATION RULES
1. Choosing the right set of
items
2. Generating rules by
deciphering the counts in
the co-occurrence matrix
3. Overcoming the practical
limits imposed by
thousands or tens of
thousands of unique items
23. ASSOCIATION RULES
Support
“The support is the percentage of transactions that demonstrate the rule.”
Example: Database with transactions (customer_# : item_a1, item_a2,.. )
1: 1, 3, 5.
2: 1, 8, 14, 17, 12.
3: 4, 6, 8, 12, 9, 104.
4: 2, 1, 8.
support {8,12} = 2 (,or 50% ~ 2 of 4 customers)
support {1, 5} = 1 (,or 25% ~ 1 of 4 customers )
support {1} = 3 (,or 75% ~ 3 of 4 customers)
An itemset is called frequent if its support is equal or greater than an
agreed upon minimal value – the support threshold
24. ASSOCIATION RULES
Confidence
The confidence is the conditional probability that, given X present in a
transition , Y will also be present.
An association rule is of the form: X => Y
X => Y: if someone buys X, he also buys Y
Confidence measure, by definition:
Confidence(X=>Y) equals support(X,Y) / support(X)
28. EXAMPLE
Example: Database with transactions ( customer_# : item_a1,
item_a2, … )
Conf( {9} => {3} ) = 100%. Done.
Notice: High Confidence, Low Support.
-> Rule ( {9} => {3} ) not meaningful
29. WHAT IS ASSOCIATION RULE MINING ALGORITHM
There are a large number of them!!
They use different strategies and data structures.
Their resulting sets of rules are all the same.
Given a transaction data set T, and a minimum support and a minimum
confident, the set of association rules existing in T is uniquely determined.
Some of the proposed algorithms are:
AIS Algorithm
SETM Algorithm
Apriori Algorithm *
AprioriHybrid Algorithm.
AprioriTid Algorithm
FP growth Algorithm
31. APRIORI ALGORITHM
In computer science and data mining, Apriori is a classic algorithm for
learning association rules.
Apriori is designed to operate on databases containing transactions (for
example, collections of items bought by customers, or details of a website
frequentation).
The algorithm attempts to find subsets which are common to at least a
minimum number C (the cutoff, or confidence threshold) of the itemsets.
Apriori uses a "bottom up" approach, where frequent subsets 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.
Apriori uses breadth-first search and a hash tree structure to count
candidate item sets efficiently.
32. APRIORI ALGORITHM PSEUDOCODE
Ck: Candidate itemsets of size k
Lk : frequent itemsets of size k
L1 = {frequent items};
for (k = 1; Lk !=; k++)
Ck+1 = GenerateCandidates(Lk)
for each transaction t in database do
increment count of candidates in Ck+1 that are contained in t
endfor
Lk+1 = candidates in Ck+1 with support ≥min_sup
endfor
return k Lk;
34. GENERATE CANDIDATES
• Assume the items in Lk are listed in an order (e.g., alphabetical)
• Step 1: self-joining Lk (IN SQL)
insert into Ck+1
select p.item1, p.item2, …, p.itemk, q.itemk
from Lk p, Lk q
where p.item1=q.item1, …, p.itemk-1=q.itemk-1, p.itemk < q.itemk
• Step 2: pruning
forall itemsets c in Ck+1 do
forall k-subsets s of c do
if (s is not in Lk) then delete c from Ck+1
36. FORMULAS TO NOTE
Min_sup count = Minimum support percentage * total number of transaction in
database
Minimum confidence percentage < Confidence percentage after association rule
37. APRIORI ALGORITHM EXAMPLES
If the minimum support is 50%, minimum confidence is 50% in database D.
Illustrate the Apriori algorithm for finding frequent itemsets in D
TID Items
100 1 3 4
200 2 3 5
300 1 2 3 5
400 2 5
Database D
40. With the confidence threshold set to 50%, the Strong Association Rules are
(sorted by confidence):
1. 2^3->5 (1.0)
2. 3^5->2 (1.0)
3. 2->3^5 (0.66)
4. 3->2^5 (0.66)
5. 5->2^3 (0.66)
6. 2^5->3 (0.66)
CONTINUE…
41. PRACTICE PROBLEM
Trace the results of using the Apriori algorithm on the grocery store example with support threshold
s=33.34% and confidence threshold c=60%. Show the candidate and frequent itemsets for each
database scan. Enumerate all the final frequent itemsets. Also indicate the association rules that
are generated and highlight the strong ones, sort them by confidence.
42. SOLUTION
Support threshold =33.34% => threshold is at least 2 transactions.
Applying Apriori
Note that {HotDogs, Buns, Coke} and {HotDogs, Buns, Chips} are not candidates when k=3
because their subsets {Buns, Coke} and {Buns, Chips} are not frequent.
Note also that normally, there is no need to go to k=4 since the longest transaction has only 3 items.
All Frequent Itemsets: {HotDogs}, {Buns}, {Ketchup}, {Coke}, {Chips}, {HotDogs, Buns},
{HotDogs, Coke}, {HotDogs, Chips}, {Coke, Chips}, {HotDogs, Coke, Chips}.