Chapter 1Introduction1.1 BackgroundIn the information age, a lot of data is generated everywhere. With the arrival of IT tools, all data is collected and waiting to be converted into information and knowledge. Therefore, the information industry provides useful information in many areas such as market analysis, science, decision making and customer relations. Data mining is the integration between analytical techniques and database system. Previously, it only had database queries, data processing, or transactional processing, which did not allow users to understand the whole data at once. They cannot answer complex questions such as what are the relationships between elements in the database. The answers to these questions are more valuable to people. User needs far exceed the capabilities of the database management system due to a huge amount of data. It is therefore appropriate to discover hidden patterns and knowledge. Unfortunately, human capabilities are limited and people cannot understand a very large data set on their own. So, powerful tools are invented to help people analyze big data. If there are no powerful tools, the huge amounts of data will just be waste, because no one would want to investigate them. In order to discover hidden patterns or useful information from huge data, there is a process called “Data mining”. In the database, associations exist when many items are presented at the same time. The relationships between the elements could represent interesting results. For example, items purchased together could represent customer behavior and patients who have flu and fever should cough. Therefore, the information, which comes from the middle of the paper ...... there products in the store, but also speaks about the events in certain situations. I only focus on how market basket analysis is implemented in retail store databases. On the other hand, data mining is also a very broad field. It is the process of extracting useful information, which are correlations and patterns, from a huge data set. The result of this could answer business questions, which usually take a long time to resolve. I am only talking about the algorithm, which is linked to the analysis of the market basket, in particular the Apriori algorithm. Additionally, there are tools that are analytical tools for data mining. This research would focus solely on Weka software as a tool to analyze retail store sales data. I explain how to use Weka with sales data to find useful insights for businesses, as well as how to interpret Weka's output for business purposes..