Stream Data Mining

Data mining is a part of a process called KDD-knowledge discovery in databases. This process consists basically of steps that are performed before carrying out data mining, such as data selection, data cleaning, pre-processing, and data transformation. Association rule techniques are used for data mining if the goal is to detect relationships or associations between specific values of categorical variables in large data sets. There may be thousands or millions of records that have to be read and to extract the rules for, but the question is what will happen if there is new data, or there is a need to modify or delete some or all the existing set of data during the process of data mining. In the past user would repeat the whole procedure, which is time-consuming in addition to its lack of efficiency. From this, the importance of dynamic data mining process appears and for this reason this problem is going to be the main topic of this paper. Therefore the purpose of this study is to find solution for dynamic data mining process that is able to take into considerations all updates (insert, update, and delete problems) into account.