Abstract:

Big data comprises of huge volume of data, which is exponentially increasing with time. Since the data is too large in size; the traditional data management tools are ineffective in processing these data effectively. The big data encompasses huge count of variables, hence analyzing each of the variables at a microscopic level is not feasible, as it might consume days or even months to have a meaningful analysis. This is time-consuming and costlier. Therefore, the Dimensionality Reduction (DR) techniques can be utilized. In general, the DR is a technique for reducing the count of input variables with fewer losses. These input features can cause deprived performance for ML algorithms. This paper introduces an optimized auto-encoder based dimensionality reduction model to deal with large datasets. The weight of the auto-encoder is fine-tuned by a selfadaptive Bumble Bees Mating Optimization (SA-BBMO) algorithm, which is the conceptual upgrading of standard BBMO. Further, to validate the appropriateness of the projected dimensionality reduction model, the experiments are conducted using big datasets. The corresponding results acquired are compared over the nonlinear dimensionality reduction techniques like PCA, K-PCA, LDA etc, in terms of Reconstruction error, Convergence, V-Measures, Silhouet Coefficient and Computation Time.

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