Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

Recently, a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes. At the same time, clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it. Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset. Big data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce tool. With this motivation, this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics (MR-HDBCC). The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big data. In addition, the MR-HDBCC technique involves three distinct processes namely pre-processing, clustering, and classification. The proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique which is capable of detecting random shapes and diverse clusters with noisy data. For improving the performance of the DBSCAN technique, a hybrid model using cockroach swarm optimization (CSO) algorithm is developed for the exploration of the search space and determine the optimal parameters for density based clustering. Finally, bidirectional gated recurrent neural network (BGRNN) is employed for the classification of big data. The experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.