Big Data analysis using a metaheuristic algorithm: Twitter as Case Study

Analyzing big data is very common requirement of today; all such requirements become difficult to access when analyzing bulk of data source such as social networking sites which are having a lot of information on daily basis. Twitter is the micro blogging and popular site providing social networking services today. Analyzing big amounts of tweets from twitter to get different patterns and extract relevant information is a big challenge. Apache Spark is the platform that is used to analyze big data efficiently. Therefore, this paper proposes a novel metaheuristic method based on Particle Swarm Optimization and K-means (PSOK). The main idea of our proposed method is to find the optimum cluster-heads from the streaming tweets. The efficiency of our proposed method has been tested on different Twitter datasets by comparing our results with Particle Swarm Optimization (PSO) and Cuckoo Search with K-means (CSK) methods.