New Technique to Deal with Dynamic Data Mining in the Database

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 [1, 2]. 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. Also real-world databases are highly susceptible to noise, missing, and inconsistent data due to their typically huge size, often several gigabytes or more. The questions here are; how can the data be preprocessed in order to help improve the quality of the data, and consequently the mining results? How can the new updated data be preprocessed in order to help improve the quality of the data, efficiency, and simplify of the mining process? Therefore the purpose of this study is to find solutions (Item-summation) for dynamic data mining process that is able to take into considerations all updates (insert, update, and delete problems) into account.