Volume - 13 | Issue-1
Volume - 13 | Issue-1
Volume - 13 | Issue-1
Volume - 13 | Issue-1
Volume - 13 | Issue-1
High-dimensional datasets were made possible by the growing popularity of bioinformatics as a topic of study and the abundance of data in many other fields. High-dimensional datasets have a small number of rows and a large sum of characteristics. Datasets with high dimensionality make it difficult to extract useful information from them. In this research, we introduce a hybrid meta-heuristic-based clustering tactic for efficiently discovering massive common patterns in high dimensional datasets. In this research, we introduce the Harris Hawks Optimizer with Arithmetic Optimization Procedure, a novel hybrid metaheuristic method (HHO-AOA). The idea is to use it to group the massive pattern. The HHO and AOA are used in a coordinated fashion to create the suggested hybrid algorithm. The devised approach is supposed to improve solution accuracy during optimisation by expanding the pool of possible solutions. The assessment criteria and standard statistical tests are used to confirm the results. Using a hybrid model shortens the time it takes to look for results in a database. At last, a massive cluster is built, and from it, massive patterns emerge. We conduct the tests on many high-dimensional datasets and utilise a wide range of efficiency measures. The results of the studies demonstrate that the suggested approach yields notable and efficient mining outcomes.