Optimizing Cluster of Questions by Using Dynamic Mutation in Genetic Algorithm

Clustering dynamic data is a challenge in identifying and forming groups. This unsupervised learning usually leads to indirect knowledge discovery. The cluster detection algorithm searches for clusters of data which are similar to one another by using similarity measures. Optimizing the clustered data with certain fixed values could be an issue. Depending on the parameters and attributes of the data, the results yielded probably either stuck in local optima or bias by attributes pattern. Performing Genetic Algorithm in the data cluster may increase the probability of the questions being clustered in the optimal group cluster. Dynamic Mutation in Genetic Algorithm used as repair mechanism to ensure the cluster is optimized enough and produce optimum indexed questions set.