Market Basket Analysis

BySowjanya Alaparthi

Topics to be discussed

• Introduction to Market basket analysis

• Apriori Algorithm• Demo-1 ( Using self created table)

• Demo-2 ( Using Oracle sample schema)

• Demo-3 ( Using OLAP analytic workspace)

Introduction to Market Basket

Analysis• Def: Market Basket Analysis (Association Analysis) is a mathematical modeling technique based upon the theory that if you buy a certain group of items, you are likely to buy another group of items.

• It is used to analyze the customer purchasing behavior and helps in increasing the sales and maintain inventory by focusing on the point of sale transaction data.

• Given a dataset, the Apriori Algorithm trains and identifies product baskets and product association rules

Definitions and Terminology

• Transaction is a set of items (Itemset). • Confidence : It is the measure of uncertainty or trust worthiness associated with each discovered pattern.

• Support : It is the measure of how often the collection of items in an association occur together as percentage of all transactions

• Frequent itemset : If an itemset satisfies minimum support,then it is a frequent itemset.

• Strong Association rules: Rules that satisfy both a minimum support threshold and a minimum confidence threshold

• In Association rule mining, we first find all frequent itemsets and then generate strong association rules from the frequent itemsets

Definitions and Terminology- Continued

• Apriori algorithm is the most established algorithm for finding frequent item sets mining.

• The basic principle of Apriori is “Any subset of a frequent itemset must be frequent”.

• We use these frequent itemsets to generate association rules.

Apriori Algorithm

Ck: Candidate itemset of size kLk: Frequent itemset of size kL1={frequent items};For (k=1; Lk!=0; k++) do beginCk+1= Candidates generated from Lk;For each transaction t in the database do

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

Lk+1=candidates in Ck+1 with min_supportEndReturn UkLk;

Pictorial representation of Apriori algorithm

Step 1Scan the transaction

database to get the support S of each 1-item sets,

com pare S with m in _sup, and get a set of frequent 1-

item sets,L1

Step 2Use Lk-1 join Lk-1 to generate a candidate k-item sets. And use appiori propersty to prune the unfrequented k-item ssets

from this set.

Step 3Scan the transaction database to get the support S of each candidate k-item set in the final set, com pare S with m in_sup, and get a set of

frequent k-item sets,Lk

Step 4The candidate set = null

Step 5For each frequent

item set 1, generate all non

em pty subsets of 1

Step 6For every non em pty

subset s of 1, output the rule “s =>(1-s)” if

confidence C of the rule “s=> (1-s)”(=support S of 1/support S of s )3

NO

YES

Demo-1Installations Oracle 10g enterprise edition

SQL Plus Oracle Data Miner Client

Demo 1 - Data Preparation

• Download the sample data, which is in excel sheet.

• write macro to convert data in excel sheet to insert queries

• Create a table and execute these insert queries in SQLplus

• As we are connected to Oracle server, this table is then found in Oracle database

Demo-1 Connections

Connect Oracle Data Miner Client to Oracle Database

• Make sure the oracle listener is listening

• Database instance ‘ora478’ is started.

• The port used is 1521 • Give the hostname as oracle.itk.ilstu.edu

Demo-1• Perform the activity, after installations and connections are made.

Demo-2 ( using oracle sample schema)

• Download Oracle 10g on your system and install it

• Select the sample schema option during the custom installation

• Launch Oracle Data Miner Client• In order to use this sample scheme for our activity, we should have the system administrator privileges.

• The username is SH and password is password

Demo -2• Administrator should perform some grants in sqlplusw to build this activity. They are

alter user sh account unlock; alter user sh identified by password;

grant create table to sh; grant create sequence to sh; grant create session to sh; grant create view to sh; grant create procedure to sh; grant create job to sh; grant create type to sh; grant create synonym to sh; grant execute on ctxsys.ctx_ddl to sh;

Demo-2The points to be noted before starting the activity are:

• Make sure the oracle listener is started

• Database instance ‘ORCL’ is started.

• The port used is 1521 • Give the hostname as 127.0.0.1, which is a general hostname.

Demo-2• Finally, the results from the model are published to a table, and this table forms the raw source for the new OLAP product dimension.

• At this point there is no information relating to revenue, costs or quantity. So, we need to extend the activity beyond association analysis to OLAP.

OLAP• We have to correctly format the results obtained from Association analysis for dimension mapping in OLAP. This can be done using OLAP DML or PL/SQL.

• In our activity we create a separate dimension that can hold the results from algorithm. For each dimension we can create Levels, hierarchies, attributes and mappings.

OLAP- Analytic workspace

• Launch Analytic workspace and give the login details as

Username- sh Connection information- 127.0.0.1:1521:orcl

This connects to Oracle sample schema SH on 1521 port and local host 127.0.0.1 and orcl database instance.

Demo 3- OLAP Analytic Workspace

• Perform the activity and show the mappings

Conclusion• We have shown how Market basket analysis using association rules works in determining the customer buying patterns. This can be further extended using OLAP Analytic workspace as shown in demo-3, to add dimensions and cube to identify other measures like costs, revenue and quantity.

ReferencesBooks: • [1] Michael J. A. Berry, Gordon Linoff.”Data Mining

Techniques: For Marketing, Sales, and Customer Support (Paperback)”.

• [2] J.Han,M. Kamber(2001) “Data Mining”, Morgan Kaufmann publishers, San Francisco,CA

Links:• [3].

http://oraclebi.blogspot.com/2007/02/using-market-basket-analysis-to-add.html

• [4]. http://nymetro.chapter.informs.org/prac_cor_pubs/Ausleder-On-market-basket-analysis-May-04.pdf

• [5]. http://www2.sas.com/proceedings/sugi28/223-28.pdf• [6] http://

en.wikipedia.org/wiki/Market_basket_analysis• [7]

http://www.cs.ualberta.ca/~zaine/courses/cmput499/slides/lect10/sld053.htm

• [8] http://www.icaen.uiowa.edu/~comp/Public/Apriori.pdf

Questions??