ABSTRACT

Due to technology Advancements, relationship marketing has become a reality in recent years. Technologies such as data warehousing, data mining, and campaign management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data mining the extraction of hidden and useful information from a large  set databases companies can identify valuable customers, predict future behaviors based on the previous purchase pattern of the customer, and enable companies to make proactive, knowledge-driven decisions.

we propose a system that will help retailers to understand dependencies among goods purchased by the customer , also knowing what good is purchased with the other or if a particular set of goods are purchased so as to maximize profit.

TABLE OF CONTENT

CHAPTER ONE

1.0      INTRODUCTION

1.1            PROBLEM  STATEMENT

1.2            SIGNIFICANCE OF THE STUDY  

1.3            AIM AND OBJECTIVE OF THE STUDY

1.4            METHODOLOGY

1.5       SCOPE OF THE STUDY

1.6     LIMITATION OF THE STUDY

CHAPTER TWO

LITERATURE REVIEW

1.1            INTRODUCTION

2.2     BACKGROUND OF THE PROBLEM AREA

2.3     APRIORI ALGORITHM        

2.4     ASSOCIATION RULE

2.5                      MARKET BASKET ANALYSIS

2.6              ADVANTAGES OF MARKET BASKET ANALYSIS

2.7              DISADVANTAGES MARKET BASKET ANALYSIS

2.8     REVIEW OF RELATED WORKS

CHAPTER THREE

SYSTEM DESIGN/ DESIGN METHODOLOGY

3.0     INTRODUCTION

3.1     SYSTEM ANALYSIS

3.2     WATERFALL MODEL

3.3     PROPOSED SYSTEM DESIGN

3.4     PROGRAMMING LANGUAGE

3.3    SYSTEM DESIGN

3.4.2  USER REQUIREMENTS                

3.5     DESIGN APPROACH

3.5.1  USE CASE DIAGRAM

3.5.2    SEQUENCE DIAGRAM

3.5.3    FLOW CHART DIAGRAM

3.6 DATABASE

3.6.1  DATABASE DESIGN

3.6.2 TABLES

CHAPTER FOUR

SYSTEM IMPLEMENTATION AND TESTING

4.0     INTRODUCTION

4.3     PROGRAM TESTING

4.4     SOFTWARE TESTING

4.5                      SYSTEM DESIGN DIAGRAM

4.6     CHOICE OF PROGRAMMING LANGUAGE

4.7                      PROGRAMMING ENVIRONMENT

4.7.1       HARDWARE REQUIREMENT

4.7.2  SOFTWARE REQUIREMENT

4.8            SYSTEM IMPLEMENTATION

CHAPTER FIVE

5.0     INTRODUCTION

5.1     CONSTRAINTS OF THE STUDY

5.2     SUMMARY

5.3     CONCLUSION

5.4     RECOMMENDATIONS

CHAPTER ONE

1.0   INTRODUCTION

Data mining is described as the extraction of hidden helpful information from a collection of huge databases, data mining is also a technique that encompasses an enormous form of applied mathematics and compultational techniques like link analysis,clustering, classification, summarizing knowledge , regression analysis and so on. data mining tools predict future trends and behaviors, permitting businesses to create knowledge-driven selections. The machine-driven, prospective analyses offered by data mining move on the far side the analyses of past events. data mining tools provides answer  to business questions that were time consuming. They search databases for hidden patterns, finding useful information that is beyond the reach of specialists.

Data mining techniques is enforced speedily on existing package and hardware platforms to reinforce the worth of existing information resources, and might be integrated with new product and systems as they're brought. once enforced on high performance client/server or multiprocessing computers, data mining tools will analyze huge databases to provide answers to questions such as, ”What goods consumers tend to buy the most  and goods that go along side with it”.

Coenen(2010) in his publication” Data Mining: Past, Present and Future” discussed the history of data mining can be dated as far back as  late 80s when the term began to be used, at least within the research community and diffrentiated it from sql.

Broadly data mining can be defined as as set of mechanisms and techniques, realised in software,

to extract hidden information from data. However,the word hidden in this definition is important;

By the early 1990s data mining was commonly recognised as a sub process within a larger process called Knowledge Discovery in Databases or KDD ,the most commonly used definition of KDD is that of Fayyad et al  as “the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data.’’ (Fayyad et al. 1996).

As such data mining should be viewed as the sub-process, within the overall KDD process, concerned with the discovery of hidden information". Other sub-processes that form part of the KDD process are data preparation (warehousing, data cleaning, pre-processing,and so on) and the analysis/visualisation of results. For may practical purposes KDD and data mining are seen as synonymous, but technically one is a sub-process of the other. The data that data mining techniques were originally directed at was tabular data and, giventhe processing power available at the time, computational eficiencywas of significant concern. As the amount of processing power generally available increased, processing became less of a concern and was replaced with a desire for accuracy and a desire to mine ever larger data collections. Today, in the context of tabular data, we have a well established range of data mining techniques available.

It is well within the capabilities of many commercial enterprises and researchers to mine tabular

data, using software such as  Weka, on standard desktop machines. However,the amount of electronic data collected by all kinds of institutions and commercial enterprises,year on year, continues to grow and thus there is still a need for efective mechanisms to mineever larger data sets. The popularity of data mining increased significantly in the 1990s, notably with the establishment of a number of dedicated conferences; the ACM SIGKDD(special intrest group on knowledge discovery in data) annual conference in 1995, and the European PKDD(practice of knowledge discovery in databases) and the Pacific/Asia PAKDD(pacific asiaconference on knowledge discovery and data mining) conferences This increase in popularity can be attributed to advances in technology; the computer processing power and data storage capabilities available meant that the processing of large volumes of data using desktop machines was a realistic possibility. It became common place for commercial enterprises to maintain data in computer readable form, in most cases this was primarily tosupport commercial activities, the idea that this data could be mined often came second. The 1990s also saw the introduction of customer loyalty cards  that allowed enterprises to record customer purchases, the resulting datacould then be mined to identify customer purchasing patterns. Data mining , is the method of looking into giant volumes of data for patterns using methods like classification, association rule mining, clustering, etc.. data mining is a topic that is related to   topics like machine learning and pattern recognition. data mining techniques area unit the results of an extended process of analysis and products development.

I am in my final year. I was bright and brilliant, my family  was optimistic in me; they thought so much of me, but I had a fault. What was my fault? I hated compiler construction.  I struggled with calculations all my life. Though i have been  lucky; I did well all the same. However, I had to write my final exam. I searched for all Compiler construction past question for each year, compared, and sorted them. Guess what I discovered! Over 35% of the questions were repetitions. I had hit the jackpot. I carefully and thoroughly checked through the answer page. Therefore, I kept on revising only the repeated questions. Well, I have a good grade to show for the Data Mining I performed.

There is huge amount of data available in Information Industry. This data is of no use until converted into useful information. Analyzing this huge amount of data and extracting useful information from it is necessary. The extraction of information is not the only process we need to perform; it also involves other processes such as Data pre-processing( Data Cleaning, Data Integration, Data Transformation) Data Mining, Pattern Evaluation and Data Presentation. Once all these processes are over, we are now position to use this information in many applications such as Fraud Detection, Market Analysis, Production Control, Science Exploration etc.

1.5            PROBLEM  STATEMENT

Through in depth research and observations carried on supermarket we have discorvered that retailers are willing to know what product is purchased with the other or if a particular products are purchased together as a group of items. Which can help in their decision making  with respect to placement of  product , determining the timing and extent of promotions on product  and also have a better understanding of customer purchasing habits by grouping customers with  their transactions.

This project is aimed at designing and implementing a well-structured market basket analysis software tool to solve the problem stated above and compare the result to that of an existing software called WEKA.

1.6            SIGNIFICANCE OF THE STUDY  

1.7            AIM AND OBJECTIVE OF THE STUDY

The aim of the study  is to maximize profit for the retailers  by providing better services to the consumers

The objective of this study are:

·         Cross-Market Analysis - Data Mining performs Association/correlations between product sales.

·         Identifying Customer Requirements - helps in identifying the best products for differentcustomers. It uses prediction to find the factors that may attract new customers.

·         Customer Profiling - helps to determine what kind of people buy what kind of products.

1.8            METHODOLOGY

                               I.            Data Pre-Processing

Due to the fact that the data we are getting is a raw data,raw data in the real world  may be incomplete it has to be pre-processed the raw data has to go through data cleaning,data integration,data normarlization,data reduction because without a quality data there will be no quality mining results.

Ø  data cleaning:This has to do withfilling of missing values, resolving of inconsistencies in the raw data.

Ø  data integration:combining data from multiple sources and generating the user with unified view of the data

Ø  normarlization: normalization is used to minimize or to reduce redundancy.

Ø  data reduction:reduction of the data set that is much smaller in volume but yet yields the same analytical results

1.5          SCOPE OF THE STUDY

This scope of the study focuses on Babcock Ventures supermarket and the scope of this project includes:

1.       We aim to develop our very own market basket analysis software, which will be used in babcock university

2.      The software will exhibit a colorful GUI(graphical user interface).

3.      The software will be based on Apriori .

4.      We intend to conduct a research into the various branches of science that this software will be based on, such as artificial intelligence.

5.       We will develop a software that will eventually stand out among other data mining software.

1.6       LIMITATION OF THE STUDY

The limitations of this software will include:

2.      Data restrictions:this is a major factor that stands in the way of the execution of this project.Since there is no data on households and individual consumers ,we neglect such purchases.

3.      Time constraints: this is also a major factor due to the fact that it can’t work on a small amount of raw data because it tends to mislead the retailer in a nut shell this software will work on large volumes of data.

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