Hadoop framework integrated hybrid optimization algorithm for privacy preserved clustering mechanism

Big data analysis has gained immense attention throughout classical techniques, which connect in mining the hidden samples from huge data. To relieve computational complexity, the clustering technique is adapted as an imperative part. A novel model is devised for privacy preserved clustering of data with MapReduce framework. The aim is to devise an optimization technique for privacy preservation. The input data is acquired from various distributed sources. The data is further partitioned and fed to MapReduce framework, which consist of mapper and reducer. The mappers perform privacy preservation by encrypting the data with several functionalities, like encryption, Kronecker product and secret key. Here, the secret key generation is performed using proposed Chimp Grey Wolf Optimization (ChGWO) algorithm. The proposed ChGWO is developed by combining Chimp Optimization algorithm (ChOA), and Grey Wolf Optimizer (GWO). The fitness is newly developed considering utility and privacy. The privacy is Jaro Winkler similarity and utility is accuracy. Finally, the data clustering is carried out with the Deep Fuzzy Clustering (DFC). The proposed ChGWO offered enhanced efficiency with highest utility of 92.5%, highest privacy of 91.5% and highest random coefficient 65.9%.

Big data clustering using fractional sail fish-sparse fuzzy C-means and particle whale optimization based MapReduce framework

The process of retrieving essential information from the dataset is a significant data mining approach, which is specifically termed as data clustering. However, nature-inspired optimizations are designed in recent decades to solve optimization problems, particularly for data clustering complexities. However, the existing methods are not feasible to process with a large amount of data, as the execution time taken by the traditional approaches is larger. Hence, an efficient and optimal data clustering scheme is designed using the devised Fractional Sail Fish-Sparse Fuzzy C-Means + Particle Whale optimization (FSF-Sparse FCM + PWO) based MapReduce Framework (MRF) to process high dimensional data. Theproposed FSF-Sparse FCM is designed by the integration of Sail Fish Optimization (SFO) with fractional concept and Sparse FCM. The proposed MRF poses two functions, such as the mapper function and reducer function to perform the process of data clustering. Moreover, the proposed FSF-Sparse FCM is employed in the mapper phase to compute the cluster centroids, and thereby the intermediate data is generated. The intermediate data is tuned in the reducer phase using Particle Whale Optimization (PWO), which is the integration of Particle Swarm Optimization (PSO) and Whale optimization algorithm (WOA). Accordingly, the optimal cluster centroid is computed at the reducer phase using the objective function based on DB-Index. The proposed FSF-Sparse FM + PWO obtained the highest accuracy of 0.903 and lowest DB-Index of 39.07.

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.