KNOWLEDGE DISCOVERY: A METHODOLOGY FOR DYNAMIC AND STATIC DATA MINING

Data mining is a part of a process which consists basically of steps that are performed before carrying out data mining, such as finding the patterns, associations or relationships among data, data selection, data cleaning, pre-processing, and data transformation using different analytical techniques involving the creation of a model and the concluded result will become useful information. This process is called KDD-knowledge discovery in databases. Technically, data mining is the process of finding correlations or patterns among various fields in large relational databases. Thus Data mining is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Therefore the purpose of this study is to find solution for dynamic data mining process that is able to take into considerations all updates into account.