The relational database model is the most frequently used database model today. With all the strengths it has, doesn’t perform well with complex queries and as well as analyzing very large sets of data. As computers have grown more potent, resulting in the possibility to store very large data volumes, the need for efficient analysis and processing of large data sets has emerged. When analyzing database performance, the disk I/O is a problematic bottleneck since disk access results in a high latency compared to memory access. A solution widely used for minimizing disk access is clustering [7]. The basic principle of clustering is to organize the data in a way that makes sure that records that are expected to be queried together are physically stored together. This means that large blocks of data can be read once, instead of accessing the disk once for each record, which would be the case if the data were spread out. This paper is intended to study the and compare different clustering techniques used to retrieve the data

Full text: IJEIR_38_Final.pdf