An adaptive clustering and classification algorithm for Twitter data streaming in Apache Spark

Raed A. Hasan, Royida A. Ibrahem Alhayali, Nashwan Dheyaa Zaki, Ahmed Hussien Ali


On-going big data from social networks sites alike Twitter or Facebook has been an entrancing hotspot for investigation by researchers in current decades as a result of various aspects including up-to-date-ness, accessibility and popularity; however anyway there may be a trade off in accuracy. Moreover, clustering of twitter data has caught the attention of researchers. As such, an algorithm which can cluster data within a lesser computational time, especially for data streaming is needed. The presented adaptive clustering and classification algorithm is used for data streaming in Apache spark to overcome the existing problems is processed in two phases. In the first phase, the input pre-processed twitter data is viably clustered utilizing an Improved Fuzzy C-means clustering and the proposed clustering is additionally improved by an Adaptive Particle swarm optimization (PSO) algorithm. Further the clustered data streaming is assessed utilizing spark engine. In the second phase, the input pre-processed Higgs data is classified utilizing the modified support vector machine (MSVM) classifier with grid search optimization. At long last the optimized information is assessed in spark engine and the assessed esteem is utilized to discover an accomplished confusion matrix. The proposed work is utilizing Twitter dataset and Higgs dataset for the data streaming in Apache Spark. The computational examinations exhibit the superiority of
presented approach comparing with the existing methods in terms of precision, recall, F-score, convergence, ROC curve and accuracy.


classification; clustering; data streaming; optimization; pre-processing

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