Enhancing Big Data Clustering Through Metaheuristic Ant-Bee Colony Algorithms

Clustering is a basic tool in data analysis and interpretation. Traditional clustering algorithms must improve on problems such as scalability for companies with huge datasets or poor-quality clusters. It has driven the research towards exploring different methodologies like meta-heuristic algorithms for enhancing big data clustering performance. One promising meta-heuristic approach is Ant-Bee Colony algorithms, inspired by foraging behavior in ants, which have successfully solved combinatorial optimization problems. Antbee colony algorithms are based on a population of artificial ants and bees searching for optimal global solutions using local pheromone communication and the exchange of information. This allows MNIST-like models to scale up on large datasets and complex clustering problems by using information about the global structure of data and just local features. Furthermore, they can deal with streaming data and non-stationary environments that work well in dynamic data to address bigdata challenges. This paper proposes an improved big data clustering approach integrating ant-bee colony algorithms to accomplish that. Predominantly, we use a hybrid technique involving both ant collection optimization and bee gathering optimization to overcome the barriers of conventional clustering methods. This research proposes overcoming clustering big macroscopic data issues using the powerful search ability and versatility of ant-bee population algorithms.