Date Added: Jun 2012
With the rapid rise of possibilities to collect and to store large amounts of data electronically tools for the data analysis also have gained increasingly in importance. This magnitude of data can contain a lot of potentially important knowledge which, however, must be firstly extracted from data within the scope of a data mining process. In general, a database system will not operate properly if it exist some null values of attributes in the system. Traditional clustering methods were developed to analyze complete data sets. Faults during the data collection, data transfer or data cleaning often lead to missing values in data so that common clustering methods cannot be used for the data analysis.