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The highly technological era that we live in has made it possible for companies to gather enormous quantities of data. Data mining is becoming more and more common for many businesses worldwide. The large amount of data that is being gathered on a daily basis captures useful information across different aspects of every business. The collection of data on a highly disaggregate level is seen as a raw material for extracting knowledge. While some facts can be revealed directly from disaggregate data, often we are interested to find hidden rules and patterns. Non-trivial insights can be generated through data mining. Data mining contains of various statistical analyses that reveal unknown aspects of the data. Mining tools have been found useful in many businesses for uncovering significant information and hence, providing managers with solutions for complicated problems.
Data mining is commonly seen as a single step of a whole process called Knowledge Discovery in Databases (KDD). According to Fayyad et.al, ‘KDD is the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data.’ (Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth, 1996)
Data mining is a technique that encompasses a huge variety of statistical and computational techniques such as: association-rule mining, neural network analysis, clustering, classification, summarising data and of course the traditional regression analyses.
Data mining gained popularity especially in the last two decades when advances in computing power provided us with the possibility to mine voluminous data. Extracting knowledge and hidden information from data using a whole set of techniques found its applications in various contexts. Knowledge discovery is widely used in marketing to identify and analyse customer groups and predict future behaviour. Data mining is an effective way to provide better service to customers and adjust offers according to their needs and motivations.
Data Mining, also known as Knowledge Discovery in Databases (KDD), is to find trends, patterns, correlations, anomalies in these databases which can help us to make accurate future decisions. However data mining is not magic. No one can guarantee that the decision will lead to good results. Data Mining only helps experts to understand the data and lead to good decisions. Data Mining is an intersection of the fields Databases, Artificial Intelligence and Machine Learning.
1.2 Problem Definition
In the recent years analysing shopping baskets has become quite appealing to retailers. Advanced technology made it possible for them to gather information on their customers and what they buy. The introduction of electronic point-in sale increased the use and application of transactional data in market basket analysis. In retail business analysing such information is highly useful for understanding buying behaviour. Mining purchasing patterns allows retailers to adjust promotions, store settings and serve customers better.
Identifying buying rules is crucial for every successful business. Transactional data is used for mining useful information on co-purchases and adjusting promotion and advertising accordingly. The well-known set of beer and diapers is just an example of an association rule found by data scientists.
The main objective of the thesis is to implement a well market basket analysis tool to see how different products in a market shop assortment interrelate and how to exploit these relations by marketing activities. Mining association rules from transactional data will provide us with valuable information about co-occurrences and co-purchases of products. Some shoppers may purchase a single product during a shopping trip, out of curiosity or boredom, while others buy more than one product for efficiency reasons.
Due to the commonly identified problem and issues in market basket analysis system, this thesis will go a long way to bring to the knowledge of sellersmining purchasing patterns of customers, store settings and serve customers better. The proposed system will be able to products with affinity to be sold together, also, improve in-store settings and optimise product placement and control inventory based on product demand. Being able to find the probability of purchase for each product or a certain set of products.Finally, the expected contribution and knowledge of this research work will benefit the researcher, and the end users.
1.4 Aim and Objectives
Over the past two decades a lot of attention has been devoted to the subject of data mining. While retailers are involved in this topic because of the absolute utility of market basket data, market analysts are interested because of the research and technical challenges they face while analysing the data.
This thesis aim is to maximize profit for the retailers by providing an automated market basket analysis system to offer a better services to the consumers.
The objective of this study are:
1. To set a system that will find products with affinity which are to be sold together.
2. Analyse customers buying habits by finding association between the different items each customer place in their "shopping Basket".
3. To improve in-store settings and optimise product placement.
4. To control inventory based on product demand.
5. To increase the amount of data that is been generated every second and to allow experts to search for meaningful associations among customer purchases.
6. To set a cross-market analysis performs for data mining,association or correlations between product sales.
1.5 Scope of Study
This thesis will cover the analysis of a market place on its market basket analysis,the market data mining, identifying customer requirements and customer profile system mapping. This system is basically a market analytical software or market utility tools which shall revolve on the listed objectives and scope
In other to archive the stipulated objectives and aim of this thesis, the following methodology was applied by the researcher to help get the optimal goal of this thesis.
1.6.1. Process Mapping
A develop detailed process map for the different market place was set in order to construct a hierarchical structure that will help in understanding the analytical structure for the association rules workflow. The map will highlight the flow from the customer’s choice of order and preference and order report from which a better analysis and decision can be made.
Figure 1:1 Mapping Process for association rule workflow
After a process map has been developed then the areas in the basket analysis will be identified and thealgorithm will be constructed.
1.6.2 Model Construction
With a process map done and market order map known an IDEF0 model will be used as a visual modelling tool to show the hierarchal relationships of all the process. The IDEF0 model will help in defining the model requirements of the current system and the proposed systems. From the IDEF0 a data model will be constructed using IDEF1X principles.
Figure 1:2 system model (Market Basket Analysis)
1.6.3 Research Methodology
The aim of this research is to automate market basket analysis so that a better decision will be made in serving the customers. IDEF will be used to model the decisions, activities and actions of the system. After a visual model of the system operations has been developed using IDEF methodologies, research will be conducted to see how market basket analysis has been used in the market place in different setting to fully capture all the realities of a physical process.
Since there is a need for a solution that will enhance the system. A research framework illustration in Figure 1.1 will be used as a visual tool to show how all the methods mentioned above will be integrated to this project to solve the problem of market place basket analysis.
Figure 1.1: Diagrammatic representation of the Research framework for the project
1.7 Arrangement of Dissertation
Figure 1.3: Flow of Study Approach/structure
Figure 1:3 gives a picture of the approach taken in this study. The research is beginning with an introduction (Chapter 1) which has an overview, statement of the problems, research objectives and aim, research methodology, scope of the study and Justification. Literature Review (Chapter 2) of the automated market basket analysis system. The articles and thesis that concerned with market basket analysis is studied. This is because buildings of a model for market basket analysis the real system require in-depth understanding of the system and process involved in the system itself. (Chapter 3), here the system is design and analyze, (Chapter 4). The system out come and requirement is detailed. Finally, recommendation will be stated and discussed (Chapter 5).
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