A MULTI-OBJECTIVE GENETIC ALGORITHM FRAMEWORK USING SELF OPTIMAL CLUSTERING TECHNIQUE

The self-ideal clustering strategy is another territory of exploration in information mining. The self ideal clustering procedure builds the productivity and versatility of segment grouping and mountain grouping strategy. The idea of the self ideal grouping method utilized the idea of heuristic capacity for the choice of bunch record and focus point. This work proposed a novel self ideal grouping strategy utilizing a multi-objective Genetic Algorithm. The multi- objective Genetic Algorithm works in two-stage in the primary stage the Genetic Algorithm work for the determination of focus point and combining the bunch record esteem dependent on characterized wellness limitation esteem. In the second period of the Genetic Algorithm check the allocated number of the estimation of K for the way toward clustering and approved the grouping as indicated by the information test. The proposed calculation was executed in MATLAB programming and utilized some rumored datasets from the UCI.