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Market Basket

Analysis

Software requirement specification

MADHAV INSTITUTE OF

TECHNOLOGY AND SCIENCE

Gwalior-474005

M.P.

PRO

JEC

TGUIDE:

Prof.Ak

hilesh

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Tiwari

Department of CSE & IT

MITS, Gwalior

I B M A

S

S

I G

D R

u O

o U

u P

s

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TEAM MEMBERS:

Anushri Jain

Shruti Goyal

Ashish Bandil

Ajit Singh Kushwah

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Market Basket Analysis Version I

Software Requirement Specification 30-01-2012

Assiduous Group

Table of Contents

Description Page no.

1. Introduction

1.1 Purpose 4

1.2 Scope 4

1.3 Definition, Acronyms, and Abbreviations 5

1.4 References 6

1.5 Technologies to be used 7

1.6 Overview 8

2. Overall Description

2.1 Product Perspective 8

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2.2 Software Interface 10

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Market Basket Analysis Version I

Software Requirement Specification 30-01-2012

Assiduous Group

2.

3 Hardware Interface 10

2.

4 Product Function 11

2.5 User Characteristics 11

2.

6 Constraints 11

2.7Architecture Design 12

2.

8 Assumptions and Dependencies 15

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Assiduous Group/MITS

Market Basket Analysis

Software Requirement SpecificationAssiduous Group

1. Introduction:

1.1 Purpose:

Version 1

30-01-2012

The amount of data being collected in databases today far exceeds our ability to reduce

and analyze data without the use of automated analysis techniques. Many scientific and

transactional business databases grow at a phenomenal rate. Knowledge discovery in

databases (KDD) is the field that is evolving to provide automated analysis solutions.

In view of above, purpose is to analyze market basket data for t he extraction of hidden

trends and buying behavior of customers.

1.2 Scope:

Suppose as a manager of an All Electronics branch, you would like to learn the buying

habits of your customers. Specifically, you wonder, Which groups or sets of items are

customers likely to purchase on a given trip to the store? To answer your question

market basket analysis may be performed on the retail data of customer transactions at

your store. The results may be used to plan marketing or advertising strategies, as well as

catalog design. For instance market basket analysis may help managers design different

store layouts. In one strategy, items that are frequently purchased together can be placed

in close proximity in order to further encourage the sale of such items together.

Although Market Basket Analysis conjures up pictures of shopping carts

and supermarket shoppers, it is important to realize that there are many other areas in

which it can be applied. These include:

Analysis of credit card purchases.

Analysis of telephone calling patterns.

Identification of fraudulent medical insurance claims.

(Consider cases where common rules are broken).

Analysis of telecom service purchases.

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Version I

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1.3 Definitions, Acronyms, and Abbreviations:

1.3.1Data Mining

Data Mining refers to extracting or "mining" knowledge from huge amount of data. Many

people treat data mining as a synonym for another popularly used term, knowledge

discovery in databases, or KDD. Data Mining is simply an essential step in the process of

knowledge discovery in databases.

1.3.2Data Mining Techniques

Fast technological changes and related research has led to the development of many

data mining techniques and systems. Because of the inherent differences in the data

model, specific techniques are developed to mine different types of databases. Different

classification schemes have been used in the literature to categorize data mining

methods based on the kind of databases to be studied (Such as transactional databases,

relational databases, spatial databases, temporal databases, multimedia databases, and

Internet information databases etc.), the kind of technique to be utilized (such as

autonomous knowledge miner, data driven miner, and query driven miner etc.) and the

kind of knowledge to be discovered. According to the last classification scheme the

following are the common data mining techniques:

Association Rule Classification

Clustering

Sequence Rule

Generalization and Summarization etc.

Since the proposed project is related to Association Rule Mining, a brief description of

Association Rule Mining is given below.

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1.3.3 Association Rule

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Mining association rules in transactional or relational databases has recently attracted a

lot of attention in databases communities. The task is to find interesting associations or

correlations among a large set of data i.e. to identify sets of items or predicates that

frequently occurs together and then formulate rules that characterize their relationship.

For example one may find, from a large set of transaction data, such an association rule

as if a customer buys "X", he/she usually buys "Y", in the same transaction. Here "X" and

"Y" are individual items or set of items. Retail stores frequently use association rules in

order to assist marketing, advertising, floor-management and inventory control etc.

Although they have a direct applicability to retail business, they can also be used for

other purposes.

A formal statement of the association rule problem is given in [1].

Let I = { i1, i2, i3, i4,.......,im}, I , be a set of m distinct literals called items.Let D be a set of transaction (variable length) over I. Each transaction contains a

set of items i1, i

2, i

3, i

4,............,i

kI.

An association rule is an implication of the form XY, where X, Y I and

XY = . Here 'X' is called the antecedent or body and 'Y' is called consequent

or head of the rule.

1.4 References:[1] R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of

items in Large Databases. In proceedings of ACM-SIGMOD International conference on

management of Data, Washington D.C., May 1993, pp 207-216.

[2] Abraham Silberschatz, Henry F. Korth and S. Sudarshan. Database System

Concept. The McGRAW HILL Companies Fifth Edition 2006.

[3]

Margaret H. Dunham. Data Mining, PEARSON Education Sevent Edition 2005.

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Software Requirement Specification

Assiduous Group

1.5 Technologies to be used:

Version I

30-01-2012

J2EE: (Servlet, JSP, JAX Java Platform, Enterprise Edition or Java EE is a widely

used platform for server programming in the Java programming language. The Java

Platform (Enterprise Edition) differs from the Java Standard Edition Platform (Java SE)

in that it adds libraries which provide functionality to deploy fault-tolerant, distributed,

Multi-tier Java software, based largely on modular components running on an application

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serverP, Java Beans)

JAVA: Application architecture. Java is an object-oriented programming language

developed by Sun Microsystems a company best known for its high end UNIX

workstations. Java language was designed to be small, simple, and portable across

platforms, operating systems, both at the source and at the binary level, which means that

Java programs (applet and application) can run on any machine that has the Java virtual

machine (JVM) installed.

DB2 9.7: IBM Database. DB2 Database is the database management system that delivers a

flexible and cost effective database platform to build robust on demand business

applications and supports the J2EE and web services standards.

RAD 7.0: Development tool. IBM Rational Application Developer for WebSphere

Software is an integrated development environment (IDE), made

by IBM's Rational Software division, for visually designing, constructing, testing, and

deploying Web services, portals, and Java (J2EE) applications.


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1.6 Overview:

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-I- Overall Description: Processes during the tenure of project -

(i) Study of Apriori Algorithm

(ii) Data Collection

(iii) Implementation of Apriori Algorithm

(iv) Development of user interface

(v) Application of Apriori on collected market basket data

(vi) Analysis of results

-I- Specific Requirements:

Real-life dataset (Market Basket Data)

2. Overall Description:

2.1 Product Perspective:

Client Tier

It implements the "look and feel" of an application. It is responsible for the presentation

of data, receiving user events and controlling the user interface. Most ecommerce

applications are web-based. The programming languages used are the combination of

HTML, CSS and Javascript.

Application Tier

This layer implements the business logic of the applications. It is usually powered by a

Java Application Server (WebSphere). There're several sub-layers within the application

layer.

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Assiduous Group

Data Tier

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This is the layer that manages the persistence of application information. It is

usually powered by a relational database server ( MS SQL Server).

Stored Procedures and Functions are used to execute database server-side

processes pertinent to data integrity. Business logic processes should be part of

application layer in general, not part of data layer.

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Fig I : Object Oriented Scenario(of three tier architecture)


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2.2 Software Interface:

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Front End Client: HTML , Dream Weaver

Web Server: Apache, Tomcat, Web Sphere.

Back End: DB2 9.7

2.3 HardwareInterface:

Minimum

Requirements:

Client Side

Processor

Intel Pentium III or

RAM

Disk Space

Internet Explorer 6 AMD 800 MHz 128 MB 100 MB

S

erver Side

P

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Intel Pentium III or

RAM Disk SpaceWeb Sphere AMD 800 MHz 1 GB 3.5 GB

Data Tier

Processor

Intel Pentium III orDB 2

RAM

256 MB

Disk Space

500 MB


AMD 800 MHz

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2.4 Product Functions:

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Developed /proposed product will include the following functions-

1.Specify input data: Define the data to be mined, data may be in the form of

dataset file or any other file etc.

2.Process data/ preprocess the input data:3.Select technique/algorithm: Select the appropriate data mining algorithm.

4.Work on results: Select visualization tools to analyze the result.

2.5 User Characteristics:

Users can be characterized as:

1. General (Non Technical User): This category includes general users having no

technical information.

2. Technical User: This category includes users having technical information.

3. Analyst: This category includes users having the ability to analyze the data as well

as result.

2.6 Constraints:

Proposed application requires user-specified Support and Confidence framework as

constraints, description of which is as follows-

Support and Confidence Framework:

Support: The first number is called the support for the rule. The support is simply the

number of transactions that include all items in the antecedent and consequent parts of the

rule. (The support is sometimes expressed as a percentage of the total number of records

in the database.)

measure of how often the collection of items in an association occur together as a

percentage of all the transactions

In 2% of the purchases at hardware store, both pick and shovel were

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bought

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Version I

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Rules originating from the same itemset have identical support but can have

different confidence

support = #tuples(LHS, RHS)/N

Confidence: The other number is known as the confidence of the rule. Confidence is the

ratio of the number of transactions that include all items in the consequent as well as the

antecedent (namely, the support) to the number of transactions that include all items in

the antecedent.

confidence of rule B given A is a measure of how much more likely it is that B

occurs when A has occurred

100% meaning that B always occurs if A has occurred

confidence = #tuples(LHS, RHS) / #tuples(LHS)

Example: bread and buttermilk [90%, 1%]

For example, if a supermarket database has 100,000 point-of-sale transactions, out of

which 2,000 include both items A and B and 800 of these include item C, the association

rule "If A and B are purchased then C is purchased on the same trip" has a support of 800

transactions (alternatively 0.8% = 800/100,000) and a confidence of 40% (=800/2,000).

One way to think of support is that it is the probability that a randomly selected

transaction from the database will contain all items in the antecedent and the consequent,

whereas the confidence is the conditional probability that a randomly selected transaction

will include all the items in the consequent given that the transaction includes all the

items in the antecedent.

2.7 Architecture Design:

Architecture of our developed product is inspired with the 3-tier architecture.

The architecture of a database system is greatly influenced by the underlying computer

system on which the database system runs.Database systems can be centralized, or client

server, where one server machine executes work on behalf of multiple client machines.

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Assiduous Group

Version I

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In case of three tier architecture, the client machine acts as merely a front end and does

not contain any direct database calls. Instead, the client ends the communication with an

application server, usually through a forms interface. The application server in turn

communicates with a database system to access data. The business logic of the

application, which says what actions to carry out under what conditions, is embedded in

the application server, instead of being distributed across multiple clients. Three tier

applications are more appropriate for large applications, and for applications that run on

the

World Wide Web.

The architecture is given in [2].

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Fig 2: Three tier architecture


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Client Tier

Version I

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It implements the "look and feel" of an application. It is responsible for the presentation

of data, receiving user events and controlling the user interface. Most ecommerce

applications are web-based. The programming languages used are the combination of

HTML, CSS and Javascript. JSP or ASP are used for dynamic content.

HTML is a Web authoring markup language for defining content structures and

rendering a web page.

Javascript is commonly used for client-side validation. Javascript does have some

control over the look-and-feel of a page in dynamic HTML.

Application Tier

This layer implements the business logic of the applications. It is usually powered by a

Java Application Server (WebLogic or WebSphere). There're several sub-layers within

the application layer.

Control Layer is the interface layer between presentation tier and application tier.

The implementation of this layer is dependent on the languages used for

implementing the presentation tier.

Transaction Layer usually implements business processes that may involve many

business objects. In J2EE architecture, session beans are commonly used for

implementing the transaction layer. Transaction Layer and Business Object Layer

are not constrained by the programming languages for the presentation and the

database used for persistence.

Business Object Layer consists of objects that represent business entities which

always should be 100% independent of database used for data persistence.

Data Access Object (DAO) Layer is the interface between the application tier and

persistence tier. Besides the methods for "creating", "retrieving", "updating" and

"removing" a business object from database, DAO objects implement other

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Version I

30-01-2012

business-specific methods as well. Even with JDBC, DAO objects may not be

100% database independent.

Data Tier

This is the layer that manages the persistence of application information. It is

usually powered by a relational database server (MS SQLServer

Stored Procedures and Functions are used to execute database server-side

processes pertinent to data integrity. Business logic processes should be part of

application layer in general, not part of data layer.

2.8 Assumptions and Dependencies:

Support : The first number is called the support for the rule. The support is simply the

number of transactions that include all items in the antecedent and consequent parts of the

rule. (The support is sometimes expressed as a percentage of the total number of records

in the database.)

measure of how often the collection of items in an association occur together as a

percentage of all the transactions

In 2% of the purchases at hardware store, both pick and shovel were

bought

Rules originating from the same itemset have identical support but can have

different confidence

support = #tuples(LHS, RHS)/N

Confidence : The other number is known as the confidence of the rule. Confidence is the

ratio of the number of transactions that include all items in the consequent as well as the

antecedent (namely, the support) to the number of transactions that include all items in

the antecedent.

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Version I

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confidence of rule B given A is a measure of how much more likely it is that B

occurs when A has occurred

100% meaning that B always occurs if A has occurred

confidence = #tuples(LHS, RHS) / #tuples(LHS)

Example: bread and buttermilk [90%, 1%]

For example, if a supermarket database has 100,000 point-of-sale transactions, out of

which 2,000 include both items A and B and 800 of these include item C, the association

rule "If A and B are purchased then C is purchased on the same trip" has a support of 800

transactions (alternatively 0.8% = 800/100,000) and a confidence of 40% (=800/2,000).

One way to think of support is that it is the probability that a randomly selected

transaction from the database will contain all items in the antecedent and the consequent,

whereas the confidence is the conditional probability that a randomly selected transaction

will include all the items in the consequent given that the transaction includes all the

items in the antecedent.

Assiduous

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Group/MITS

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Assiduous Group

Version I

30-01-2012

Special Thanks

We convey a special thanks to our department and to our

college. We also convey a special thanks to all these

softwares and websites, they have been helping a lot in

doing the project.

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Group/MITS

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Documents

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