On fly hybrid swarm optimization algorithms for clustering of streaming data
Yashaswini Gowda N, B.R. Lakshmikantha
Abstract
Clustering is an important data analysis technique for extracting knowledge and hidden patterns in the data. Recently hybrid clustering algorithms have been proposed to solve the local optimum and poor robustness problem due to improper selection of initial cluster centroids in the traditional clustering algorithms. In hybrid algorithms, clustering is solved as an optimization problem by invoking optimization algorithms to find the optimal cluster centroid. Many hybrid clustering algorithms integrating different swarm algorithms and traditional clustering algorithms have been proposed. However, these algorithms were not designed for the on-fly clustering of streaming data. This is necessary for real-time streaming requirements in financial domains like stock markets. This work proposes an on-fly hybrid swarm optimization algorithm for the clustering of streaming data.