Clustering uncertain data with possible worlds

The topic of managing uncertain data has been explored in many ways. Different methodologies for data storage and query processing have been proposed. As the availability of management systems grows, the research on analytics of un-certain data is gaining in importance. Similar to the chal-lenges faced in the field of data management, algorithms for uncertain data mining also have a high performance degradation compared to their certain algorithms. To over-come the problem of performance degradation, the MCDB approach was developed for uncertain data management based on the possible world scenario. As this methodol-ogy shows significant performance and scalability enhance-ment, we adopt this method for the field of mining on uncer-tain data. In this paper, we introduce a clustering method-ology for uncertain data and illustrate current issues with this approach within the field of clustering uncertain data.