Parallel Glowworm Swarm Optimization Clustering Algorithm based on MapReduce

Clustering large data is one of the recently challenging tasks that is used in many application areas such as social networking, bioinformatics and many others. Traditional clustering algorithms need to be modified to handle the increasing data sizes. In this paper, a scalable design and implementation of glowworm swarm optimization clustering (MRCGSO) using MapReduce is introduced to handle big data. The proposed algorithm uses glowworm swarm optimization to formulate the clustering algorithm. Glowworm swarm optimization is used to take advantage of its ability in solving multimodal problems, which in terms of clustering means finding multiple centroids. MRCGSO uses the MapReduce methodology for the parallelization since it provides fault tolerance, load balancing and data locality. The experimental results reveal that MRCGSO scales very well with increasing data set sizes and achieves a very close to linear speedup while maintaining the clustering quality.